
Ask HN: Engineers from non-CS background, how did you pivot into ML/AI? - ultrasounder
I am a EE, hardware engineer with about a decade of experience in PCB electronics and systems engineering experience which includes a brief stint at a FAANG that also happens to be an E-tailer. I have been &quot;dabbling&quot; in Python for about a year now and just recently started with DL using PyTorch and find it quite interesting. To be clear I don&#x27;t write code at work, atleast not until now. I intend to utilize any free resources (MOOCs) to teach myself the latest techniques in DL for CV. What I am not clear is the next logical step.A part of me wants to Boostrap a SAAS using Python stack to build something that I can market using the traditional channels(PH,Show HN,Reddit,answer SO questions) and show it to potential employers but am not sure if I will even get to the interview stage with a resume but that doesn&#x27;t look anything like a programmer with a tradional CS background and work experience to boot. Sorry about the long and winding question, but what should I do to get noticed by recruiters at FAANG and non-FAANG to stand apart from the CS crowd?
======
imh
Stop focusing on MOOCs and youtube videos and study textbooks. Do exercises.
Treat it like academic studying, and you'll end up with a decent education.
It's important, because it's often easier to make a thing work okay than to
understand why it works, so you'll get false confidence working through a
tutorial. But then you want to apply that to something else and it doesn't
work quite right, you won't know why it doesn't work and how to fix it.

Get some textbook suggestions and make a minimum of reading 5-10 pages per
day. In about a month or two, you're done with a 300 page book. Repeat that
for a few years and you're an expert. Once you have the foundations, read
papers too, but don't skip straight trying to using AlphaZero to solve a curve
fitting problem.

~~~
boltzmannbrain
> study textbooks. Do exercises. Treat it like academic studying

This. Highly recommend Russel & Norvig [1] for high-level intuition and
motivation. Then Bishop's "Pattern Recognition and Machine Learning" [2] and
Koller's PGM book [3] for the fundamentals.

Avoid MOOCs, but there are useful lecture videos, e.g. Hugo Larochelle on
belief propagation [4].

FWIW this is coming from a mechanical engineer by training, but self-taught
programmer and AI researcher. I've been working in industry as an AI research
engineer for ~6 years.

[1] [https://www.amazon.com/Artificial-Intelligence-Modern-
Approa...](https://www.amazon.com/Artificial-Intelligence-Modern-
Approach-3rd/dp/0136042597)

[2] [https://www.amazon.com/Pattern-Recognition-Learning-
Informat...](https://www.amazon.com/Pattern-Recognition-Learning-Information-
Statistics/dp/0387310738)

[3] [https://www.amazon.com/Probabilistic-Graphical-Models-
Princi...](https://www.amazon.com/Probabilistic-Graphical-Models-Principles-
Computation/dp/0262013193)

[4] [https://youtu.be/-z5lKPHcumo](https://youtu.be/-z5lKPHcumo)

~~~
sampo
PGMs were in fashion in 2012, but by 2014 when Deep Learning had become all
the rage, I think PGMs almost disappeared from the picture. Do people even
remember PGMs exist now in 2019?

~~~
KidComputer
You'll find plate models, PGM junk, etc in modern papers on explicit density
generative models and factorizing latents on such models.

------
tmsh
[https://aws.amazon.com/training/learning-paths/machine-
learn...](https://aws.amazon.com/training/learning-paths/machine-
learning/data-scientist/)

Patience. Spending that extra time (it helps if you really enjoy it or can
program yourself to really enjoy it).

[https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
for inspiration.

To answer the question though: I'm not sure what you produce other than maybe
blog or publish some analysis using your data skills. And maybe:
[https://github.com/MaximAbramchuck/awesome-interview-
questio...](https://github.com/MaximAbramchuck/awesome-interview-
questions#data-science)

(source: non-CS engineer at amazon who watched the amazon videos internally
before they were made public. I'm not a data scientist yet but sometimes, esp.
when people talk about the challenges of AGI, I think about transitioning.)

~~~
ultrasounder
Thanks for the links to AWS videos. These were posted here on HN sometime
back. Will definitely bookmark them and come back to them. At the
moment(literally)i am still finishing up Udacity PyTorch and hope to continue
wit the venerable DL4Coders part1 and part2. As someone else had posted,
coming up with _useful_ implementations of popular ArXIV papers seems to be
one sure shot way of building up a personal brand on Github.

~~~
jafingi
Andrew Ng's courses on Coursera (Machine Learning) are the best ones IMO.
Gives you the introductions to the math behind and some practice.

------
bhollan
I'm not sure how this will be received, but I'm learning a lot through
following Jeremy Howard. He's a huge PyTorch fan and he's spent the last 3
years trying to figure out what people like you need. He launched a course
called FastAI and a DL library by the same name. His aim is to help anybody do
it that wants to, with or without code.

MOOC: [http://course.fast.ai](http://course.fast.ai)

I just found a resource a few months ago that I'd love to recommend, but
haven't started yet. It's mentorship you pay for, but not up front. You sign a
contract to pay a certain percentage after you're hired. I plan on going
through this program if my current job leads don't pan out.

Mentorship: [https://sharpestminds.com](https://sharpestminds.com)

I'm interested in comments about either program in general. Speaking as
someone who also has an EE degree, went through a web development bootcamp,
and was disappointed by both at the help in getting hired that was offered
after the curriculum was finished, I am also interested in your findings and
results.

~~~
edouard-harris
Hey there - I'm one of the cofounders of SharpestMinds. AMA!

EDIT: Also, I strongly concur with the fast.ai recommendation for deep
learning, especially if you're starting from a background in software.

~~~
danloeb
Hi Edouard, interesting concept. Who are the mentors and why don't you list or
profile a few of them on the website? (beyond the company logos)

~~~
edouard-harris
Thanks!

Some stats about our mentors:

\- There are about 60 of them now

\- Geographic distribution is ~1/3 in the Bay Area, ~1/3 in the Toronto
region, the rest across the USA and Canada

\- About 50% are deep learning engineers, the other half are a combination of
ML devops, data eng, traditional ML (clustering, boosted trees, etc.)

\- About half work in (or are alums of) the AI labs of major companies such as
the ones whose logos are on the website

Why we haven't listed some of them on our website yet: no good reason. We'll
probably do this soon. It's a good idea.

~~~
danloeb
Thanks for the response!

------
fnbr
My background is that of an econometrician (ie quantitative economist), and I
now work as a Research Engineer at one of the FAANG research divisions.

I think the advice about getting in as a hardware engineer is solid. At my
workplace, there's a ton of need for people working on specialized hardware
for DL, and for people working on the software that works with it (optimizing
compilers, etc).

If you are looking to break into the software side of DL, the first two thirds
of the Deep Learning book [1] contains all the math you need to know to pass
the interviews. Then, it's just a matter of getting interviews; I found that I
needed professional experience deploying DL/ML to do that. I got that by doing
side projects at work. For instance, we had a long standing operations
research problem, and I spent some free time at work implementing a RL
algorithm to solve it. I didn't get too far, but I was able to talk coherently
about the papers involved and about how I planned to conduct the project,
which went a long way.

[1]: [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)

~~~
heinrichf
> Then, it's just a matter of getting interviews

Are you implying that, once prepared well enough, the contents of the
interviews are simpler than getting actually noticed in the pile of applicants
?

~~~
joshvm
You need to think like an interviewer - what can you reasonably make someone
do in half an hour (plus time for chat and questions after)? Apart from being
able to parrot deep learning theory, implementing things is tricky. Do you
learn anything from making someone implement VGG in their pet framework?
Training models also takes more time than you have to spare.

Much easier to quiz the applicant how they would solve a problem, or to
discuss a previous project or paper they've published (or are interested in).
Some people will find that much easier than whiteboard coding, others will
hate it.

It really depends where you apply and if you want an applied or research role.
Some places won't touch you unless you've got a publication in somewhere like
CVPR. Others will go _hard_ on the stats questions. Other places want to see a
strong Kaggle rank or some personal projects. It's really useful to have a
portfolio here.

~~~
heinrichf
Thanks, that is helpful advice.

------
xenihn
Maybe someone who actually works at FAANG can weigh in, but I would think that
one of your best bets would be getting into one as a general SWE and then
transitioning to AI/ML internally after a year. I recall Google even having
some sort of internal program that encouraged this. Getting into Google is a
moonshot, but it's possible to do so with no prior professional programming
experience if you put in a ton of effort AND get lucky. Amazon seems willing
to train too, based on the following experience I had:

I'm an iOS engineer without a STEM background, and I've been contacted by
Amazon recruiters for entry-level ML/AI positions. I thought it was weird, but
they said they've hired a few people with iOS backgrounds and no prior ML/AI
experience who are now excellent ML engineers. I backed out because I knew I
would fail the interview process at this point, but it's something for me to
think about for the future.

~~~
ryanjodonnell
Random side note, but when is the 'FAANG' acronym going to die? MSFT is
killing it, prob the top tech company around these days. Needs to be included
in that list.

~~~
make3
I agree, Microsoft is probably the #2 top company in AI after Alphabet, should
be included

~~~
macheen
Microsoft Research does a lot in AI but don't forget FAIR, Nvidia, Baidu, and
Amazon. Smaller companies like OpenAI are making strides too.

~~~
make3
i agree 100%

------
arcanus
I'd strongly recommend considering hardware companies in the ML space. They
are trying to acquire ML talent but will also value your EE background. This
is probably best approached at the usual big ML hardware vendors (NVDA, INTC
and AMD) but there are also numerous start ups in the space that may be
credible.

Lots of good advice about acquiring skills, I don't have much to add beyond
that. I'll just mention that before you jump into the advanced stuff, please
understand the terminology and basics very strongly. I've interviewed over
twenty people for roles in ML the last year and many (despite having ML on
their resume or even some experience in it) could not even explain the
difference between training/inference, the meaning of validation, etc. The
field is so hot right now that many unqualified folks are trying to get in,
often by faking more experience than they really have. In response, I've
created a simple 'fizzbuzz' test just so I can quickly screen people.

------
daniellemswank
I've hired about half a dozen ML engineers/architects in the past six months.
Several of them have EE backgrounds. There's a good bit of ML that touches on
hardware (think integrated cameras and similar) so it can be really helpful.

You're mostly on track with your plan to build something. You do need to
demonstrate that you have the skill set, but building one giant thing isn't
the answer. There's so much that goes into building a giant thing, that I
can't accurately access your ML skills.

Ideally I like to see a lot of small things over a reasonable amount of time.
Someone with a solid GitHub showing 6-12 months of paper implementations,
weekend geez-wiz hacks and various other projects would go right to the top of
my call back list.

Hope that helps, good luck with the job search.

~~~
ultrasounder
Thanks!. I think [https://fomoro.com/projects/project/reverse-image-
search](https://fomoro.com/projects/project/reverse-image-search) this has
provided me some motivation. Also just subscribed to your meetup. I will try
to make it to the next meetup on the 8th as i am just about starting with GANs
in my udacity PyTorch course. Thanks for organizing this meetup. I will leave
the link for folks from Bay Area to follow-up. [https://www.meetup.com/deep-
learning-sf/](https://www.meetup.com/deep-learning-sf/)

------
minimaxir
Speaking from experience, almost all FAANG positions I've seen require a
degree for ML/AI, and even require a degree for less research-oriented
positions like a Data Analyst/Scientist.

Non-FAANGs may be less picky but the competition in the field is too great at
the moment (due to MOOCs/Bootcamps increasing supply), and even with an
excellent portfolio it may be impossible to stand out. (in my case, despite my
data science "fame" most recruiters tossed my resume out immediately during my
job hunt a year ago; the only interviews I got were by going above the
recruiters. And that was for data science, not even ML/AI)

Even after working as a Data Scientist for over a year, I've received
practically no recruiter spam.

------
souljaboytylem
After reading most of the comments I can try to provide a different
perspective.

I am a Director of Data Science and Software Engineering for a mid sized firm
(~1000 employees and $150-200MM revenue). I started with a Finance degree then
shifted into an analysis position at a FAANG (lots of excel, SQL, learning how
to query big data). This eventually led to learning more about tech (python,
AWS cloud stack, messaging queues) and after 8 years in the industry giving me
enough experience to manage teams of data scientists, software engineers and
data analysts.

Although it is so important to know all the software engineering stack, many
companies will benefit from simple business intelligence and data analyst
roles. I guess my recommendation is to also keep an open mind in looking for
these types of roles in the market (data analyst, business intelligence
engineer), because given your desire to learn and existing background, its
clear you can make a big impact in those companies as well. And it will be
much less competitive than traditional CS crowd.

Some food for thought

~~~
ryanjodonnell
We gotta stop saying 'FAANG' when MSFT is the arguably the top tech company
around these days.

~~~
throwaway98121
Not disagreeing with you (not informed enough to), but top tech company by
what measure? Market valuation? Impact of products/services in 2018/2019?
Workplace rating?

~~~
Cyph0n
I’ve always felt that Microsoft fits in with GFAA much better than Netflix.

------
JoeDaDude
I am also an EE and have been able to apply ML to my field, wireless comms. It
turns out people skilled in the intersection of two fields are very rare
indeed.

I'd suggest you look for an opportunity to apply ML/CV/AI in your industry
(deep learning for PCB inspection maybe?). Show the possibilities, get some
research funding, do a pilot program or similar. Lead and drag your company
(kicking and screaming if need be) into the 21st century.

Then you will have ML/AI on your resume, and recruiters will come looking for
you.

~~~
ultrasounder
Thanks for the response.Wireless comms seems like ripe for ML enhancement. PCB
inspection is something that has crossed my mind indeed but it is quiet
involved due to the sheer number of features ( traces, components, vias,
connectors) none of which has pretrained models. Having said that Automation
esp FAI( First article inspection) is quiet possible as demonstarted by
landing.ai(Andrew NGs) company.Being employee at a publicly trading company,
not sure how I can secure funding. Perhaps my focus should be to apply ML
directly to something that I do day-day at work.

~~~
lnsru
Would you like to write an auto router for PCB design using ML? A smart one
that can indentify components, their properties and choose right connections
between them? Have a fast signal going directly and some shitty LED with 10
vias. Or placing power parts with wide tracks and using thin ones for digital
signals.

~~~
ultrasounder
Auto routers already exist and I always route my own PCBs. I am pretty sure
the tool vendors are considering or already building up AI expertise to level
up their Auto routers. I keep hearing at least in Altium they have come a long
way. Gotto try it one of these days.

------
zerr
I'd say pivoting to ML/AI is much easier for Math/Physics folks compared to
pure CS ones.

------
DrNuke
> what should I do to get noticed by recruiters at FAANG and non-FAANG to
> stand apart from the CS crowd?

The usual answer here is look for suitable business / r&d cases within your
own EE industrial domain and use ML/AI as any other tool instead of as a black
box or a magic wand. Good luck.

~~~
robertAngst
I'm just trying to get my first programming job.

I cant seem to get past HR. My resume has that I'm a Chem Engineer BS,
Industrial MS, 7 years in engineering, 2 years of Electrical Engineering.

The first page of my resume is my 10 years of non-career programming
experience. Built a Dishwasher(embedded C++), full stack app(RN JS, Mysql PHP
laravel), and smaller projects.

I cannot get past HR.

Every real life programmer I show my work to, knows I'm capable. Heck even
some got me in touch with HR. Nothing came of it.

~~~
brett40324
Off topic, but wanted to reply regarding "The first page of my resume is my 10
years of non-career programming experience."

This must be part or most of the problem. Cut your resume down to 1 page, if
possible. Include a meaningful cover letter catered to the opportunity and
specific company youre applying to. Shove the last ten years stuff into the
very end, and start that first page with your software knowledge and related
projects. Ping me someday here if this ends up getting your foot in the door.

~~~
walshemj
Why not use a pitch or capability cv? and ditch the traditional cv format

------
kk58
I built ML application in my domain which helped demonstrate my
capabilities.Networked a fair bit within my company and then got the chance to
lead a ML team. Whole process took about 2 years.meanwhile self educated
myself continuously over last 3 years. Spent min 20 hours a week coding,
reading, learning and discussing ML. Joined ML learning groups helped other
folks and learned through their journeys as well.

ML is unfortunately better done in a big company due to data but also a b
__*Ch due to tremendous friction within org to get things done.

Another key strategy is to commit yourself to build an end 2end ML
application, structure your learning around it. I found this a tremendous
technique to turbo charge my learning.

------
billman
I'm a fellow EE grad, that has been doing software for the past 20 years or
so. I started studing ML a couple years ago. I started with a couple of Andrew
Ng's courses on Coursera. I found it was a good mix of theory and practice.
It's a really exciting field right now (a bit over-hyped, but still lots of
room for growth).

BTW, I think the you may have a bit of an advantage because of the math
background you presumably have with a BSEE (linear algerbra & differential
equations).

------
majickdave
It is extremely tough. I left my Civil Engineering career path in 2015 in
search of a career in tech. I then applied to graduate school at SMU for an
online masters in data science. I am still struggling to find meaningful work,
but currently mentoring a data science bootcamp.

This is a very tough track but uf you have software development experience it
should be easier to get a role in ML or Data Science.

~~~
rustiest
How did you find that SMU program? I was debating that and a similar program
at UC Berkeley.

------
kbenson
I can't help but read the title as something akin to "musicians, how did you
become comfortable painting with watercolors?"

I know ML/AI is all the rage. I just feel that targeting it so heavily is a
bit shortsighted.

~~~
sl1ck731
I read somewhere that learning ML/AI isn't the hard part. It is having enough
data science background to be able to tell tell what problems fit ML. ML isn't
the hard part, finding a problem ML can approximate is.

Maybe that is too simplistic but I can't help after my 1 semester ML course
think that most of the ML problems people are solving aren't really suited at
all. Like SWE see this cool hammer and now everything is a nail. Maybe I
should read up on startups using it successfully for anything but I haven't
seen many of those on the frontpage.

------
omalleyt
Industrial engineer by training here, I honed my chops on Kaggle competitions,
eventually winning one. I was recently brought into a FAANG in a ML/AI
engineering capacity.

I can’t reccomend Kaggle enough for those who are looking to prove their
abilities in the field.

~~~
ultrasounder
Nice! Another pivot story. How did you make it past the Recruiters and their
filters? Just with your kaggle portfolio? Any chance you can post a link to
your kaggle profile. Congrats on winning Kaggle and that's no mean feat,
considering the competition(pun intended).

------
Waterluvian
Naive question. Is ML/AI the "real deal" and here to stay, or is it still kind
of just the hype du jour?

Or put another way: are there plenty of problems where ML/AI are valid tools
or are they largely cool tech looking for problems to fit into?

~~~
minimaxir
ML/AI is here to stay, but currently the marketing and potential impact of
ML/AI is a bit _exaggerated_.

------
sgillen
Not sure it' the best path for you (it could be though!) I'm an EE and applied
for PhD programs in control theory (robotics specifically). Now my research
focuses on applying deep learning to control under-actuated robotic systems.

Although I went right after my undergrad, there are several in my cohort who
were in industry for as long (or in one case much much longer) than you have
before starting their PhD.

There are certainly ways to merge your hardware experience with learning.
Either applying AI to hardware design, or applying hardware design to speed up
or otherwise improve learning, lots of research going on in both areas.

------
goodmattg
This is my issue with the phrase "AI/ML" as a catch all. You have an excellent
general skillset, but "AI/ML" encapsulates a wide spectrum of jobs.

ML-SWE: SWE with ML focus - building architecture around models, feature
engineering, distributed training, etc. Relatively limited ML knowledge needed
(IMO). The math won't be helpful for this role. Much more important to have
SWE background. If you want this, keep building your programming knowledge
(Python) and read books. Would focus on understanding the popular frameworks
PyTorch and TensorFlow b/c your work will likely interface with those.

Research engineer: Mostly for MS/PHD background. Farther away from the product
and closer to actual research. This doesn't sound like what you want to do.

Data Scientist: ML is a subset of the knowledge needed. Applied statistics as
important, if not more so. Doesn't sound like you want this.

A path forward:

(1) Program a lot. On what? Anything at all, b/c you need programming skill to
work as a SWE.

(2) If you want to do ML-SWE, program with an eye towards ML applications.
Maybe do a simple cloud project that leverages ML - Google Cloud makes this
particularly easy for classification tasks. Focus on breadth here, not depth.
No sane person outside of academia can keep up with state-of-the-art and truly
understand it. Far too much material, so focus on fundamentals.

(3) Work towards your strengths. You aren't some hotshot kid out of college
proclaiming to be an AI guru. That would be silly and no competent recruiter
would believe it. You know hardware - and AI (neural networks) leverages a lot
of hardware. Why not focus on the hardware side of AI? Demonstrate your
knowledge of how/why TensorFlow is so effective across distributed hardware,
or how CUDA accelerates NN computation, or why TPU claims vs. Nvidia may be up
to interpretation, etc. This should be a natural transition given your
background.

TLDR; Know what you really want to do. Your background is valuable. Play to
your strengths. Don't ring the bell.

~~~
TrueSelfDao
Can you let me know a bit more about the SWE-focused ML path? While I'm
interested in ML, I'm slightly more interested in the systems that are built
around it. In particular, are there resources that can help me get up to speed
in designing (distributed and HW-accelerated) ML systems.

------
wenc
Not a full answer, just an observation: most ML people I come across don't
have CS backgrounds. Many have backgrounds in physics, math and other STEM
fields that have a strong computational component (my own background is in
control systems, math modeling and numerical computation).

From your question, it sounds like you want to be a software engineer rather
than an ML/AI engineer -- is that a fair assessment?

~~~
ultrasounder
Its certainly good to know that ML doesn't require a CS background. In my own
case, My thinking was that because ML/AI has a strong programming component in
addition to Math&Stats, my strong Hardware background might work against me.
In an ideal scenario, i would like to develop AI/ML applications and wouldn't
mind morphing into a SW engineer if the role requires me to. THough SWE by
itself would be another steep hill to climb.

------
sonabinu
I went back to school for cal 2, Multivariable calculus, Differential
Equations and Libear Algebra. Took ML class from a local university after
that.

------
scatter
Hi, I am in a similar boat, and would love to network with you either in
person or online.

I have a PhD in EE, working in semiconductors. I have done a couple of MOOC
specializations on Coursera, and am trying get some data science projects on
my resume. Also trying do some Kernels / Scripts on Kaggle to build up a basic
portfolio.

~~~
ultrasounder
Hi, Email is in my profile and I am always looking to network. Shoot me an
email and we can take it from there.

------
macheen
You may find this post helpful
[https://www.reddit.com/r/cscareerquestions/comments/92ouzd/a...](https://www.reddit.com/r/cscareerquestions/comments/92ouzd/a_guide_to_landing_a_data_sciencemachine_learning/)

------
danbo_95
This conversation might help
[https://twitter.com/suzatweet/status/1078446189593321472](https://twitter.com/suzatweet/status/1078446189593321472)

~~~
ultrasounder
The overarching theme of that thread reflects Max Woolf's comments:
Companies(FANGMA)place a lot of importance on Accreditation and that is
pushing a lot of capable folks out of ML/AI applied roles(not talking about
inventing the next capsule network that requires a P.hD).

------
monksy
Become strong in the background of ML/AI and then learn a language where you
can use it. Demonstrate your achievements in it. Help with projects associated
with it. Participate in the community.

------
known
[https://www.datascienceatthecommandline.com/](https://www.datascienceatthecommandline.com/)
when maths is hindrance

------
asimjalis
I am curious: Why do people want to pivot? Is the work more interesting? Is
the pay higher? Are there more opportunities? Is there some other reason?

~~~
gcb0
bad management.

on FAANGs, the teams are usually huge, 100+ people doing what a nimbler
company does with 3 or less, to the point the employees don't even see that,
because the product is now broken into several pieces to give the illusion of
complexity. middle managers then break it down further that engineers start
being know as the "person that writes the java files in that one directory"
and nothing else. This creates constant fear of becoming irrelevant. All while
you see 2~10% pay raises while hearing about undergrads making the same you
make now with "new tech du jour". This creates even more pressure.

And because this cycle (stagnate, fear, learn, relief) repeats often,
engineers start to associate learning a new tech with happiness, just because
it offsets the psychological fear for awhile.

------
pps43
EE -> embedded programming -> programming -> financial software -> predictive
modeling -> ML.

~~~
expertentipp
By the time the OP gets there along this path the new hype will be on GQ/JP
already.

~~~
ultrasounder
LOL! WTH is GQ/JP btw?

~~~
expertentipp
We don't know yet.

------
agentofoblivion
I basically did what you’re talking about. Masters in physics, then went into
semiconductors as engineer and materials scientist, then switched to Data
scientist at a bank for 18 months, and now have been a research scientist in
AWS for almost two years.

In Amazon, it’s easy to move around, but not between job families. I think
it’s a bad idea to join as a SWE and try to transfer because they want people
that have done it before, and you’re unlikely to do that sort of work as an
SWE. I think it’s better to get experience in the role you want at a less
prestigious company. You’ll learn a ton. Pick the best company that will have
you.

My personal turning point was when I did free work for a local startup in
exchange for them letting me take the Data Scientist title. To a recruiter,
it’s totally obvious to hire a data scientist for a data scientist role, and
isn’t clear at all what physics has to do with it. Recruiters are the first
step when you’re starting fresh, so make it easy for them.

I also somewhat disagree with many of the comments here that textbooks are
better than tutorials. If you buy these 1000 page graduate level texts when
the idea that you need to read them cover to cover, you’re likely going to
give up and fail. Instead, buy the books and put them on the shelf, and then
work through tutorials and examples. Then reference particular sections of the
book that are relevant to your work to add depth.

Finally, I recommend against starting with deep learning. There’s a whole
helluva lot to learn with basic techniques. Very few companies are actually
using deep learning in production systems. Start with linear and tree based
methods to learn all the stuff about how to frame the problem and build robust
systems. Then you’ll have a deeper appreciation for DL.

A reasonable person could disagree and say that there’s so much domain
specific stuff around the art of DL that it really behooves you to start there
ASAP. I would counter that you’re unlikely to be considered for positions
using DL unless you’re pursuing your PhD in it, or have proven yourself in
industry. Since that isn’t your situation, I’d wait until you got your foot in
the door somewhere and then pursue DL on the side. That’s what I did, and then
you look like a hero to your boss. This strategy led to my first publication
in the field and I’m now working on DL almost exclusively.

Edit: one more thing. Think carefully about the type of work you want to do.
My advice is assuming you’d like to be a person that trains/deploys ML models
to solve problems in industry. This is much different than an ML Engineer,
who’s implementing algorithms in low level languages and squeezing out
efficiency. Obviously that would require a much deeper understanding of SWE.
And a totally different person is an academic researcher that’s developing
theory or technique. It’ll be hard to do that without a PhD.

~~~
scatter
Great reply. Just out of curiosity, did you end up giving your semiconductor
job before joining the start up with a data scientist title ?

I am deep into semiconductors, and am facing the dilemma of giving up my
expertise so far, to join a startup as an entry level engineer.

I have done a couple of MOOC specializations and am trying to find projects
within my industry to gain some credibility. Also trying to stay active on
Kaggle to build some basic data analysis portfolio.

~~~
agentofoblivion
In my particular case, I did quit my job before I got my "real" Data Science
position. I can't recall exact timelines, but I think I had already lined up
the relationship with the startup.

The reason I did this is because there just wasn't enough hours in the day,
and my job was taking ~10 hours a day with commuting...etc. It was a risk, but
the idea was that I would be able to transition much more quickly if I worked
full-time towards it. I also had the financial savings to support myself for
6-9 months and was willing to get a part time job if necessary. Once it became
clear that my job's only purpose was to pay the bills in the context of my
goals, and I had enough to pay the bills for the near future, it was clear
that quitting was the easiest way to free up a lot of time.

This turned out to be the best decision of my career, but YMMV. I doubled my
salary in less than 2 years. It's also nice to be part of an industry that
isn't so cost-sensitive. I also have a skill set that's in much higher demand,
so you can live almost anywhere and there's a ton of companies that want/need
it. With semiconductors, you're much more limited.

It's true that you're giving up some expertise and will start in a less senior
position in a different field than if you stayed in semi. Sticking around
because you have experience is classic Sunk Cost Fallacy. Think 5 years down
the line. If you leave now, you'll have 5 years experience in ML. You'll
definitely be giving something up if you leave, but there's huge opportunity
cost if you don't leave.

~~~
scatter
Would you be open to have a brief conversation on the phone for advice? My
email is knariks@gmail.com.

------
colinmegill
+1 fast.ai

