There's already a lot to learn as a part of ML, should you learn DSA for non faang companies [ML and Data Science roles] If yes, what algorithms to learn?

Hello everyone,

I am an university student who is considering a career in machine learning. I have discovered that I enjoy the process of data modelling, data engineering, building statistical models and making decisions from them much more than software engineering. However, I saw that a lot of MLE jobs are more about putting ML models into production (so more SWE), which I don't see myself enjoying.

Are there are jobs focusing mainly on ML research and model-building/optimization? I plan to do a bachelors in Industrial Engineering (which at my school is basically applied math/stats) to gain a solid foundation in optimization, big data analysis, and statistical learning then pursue a CS masters. Would these jobs be only open to PhD grads?

Any info is appreciated, thank you!

Hi, I'm a first year CS student and I've been having a few questions relating to the AI/ML field that I legit can't find the answer to anywhere unfortunately...

First, I'm heavily debating leaning my education towards AI/ML by taking more math, but specifically minoring in statistics. When going into uni, I thought I was just going to be a code demon and grind leetcode and projects. But I thought, is that really still the move? What if AI/ML is truly the future? I've been trying to do more research and can't really find any useful insight. Just wondering, if anyone thinks the SWE jobs will be cooked soon like 5+ years, and it's likely possible that AI/ML will be far superior.

Another question, what do you actually do in these new AI/ML jobs? Like I'm hearing so many different things from different people so does it just depend on the company? Everywhere I look, on YouTube, LinkedIn, personal friends... It's all so confusing, you see me refer to the term "AI/ML" and to be frank, I don't even know exactly what that means. From my understanding, an ML Engineer for example, doesn't actually work with the theory (the math and statistics) behind these models. That's the work of the Masters and or PhD people. Are ML Engineer's just SWE's but work with these pre-built/designed models? I've heard they just help train and tune the models by programming and likely other tools that I'm unaware of, but no crazy math or stats is needed I think? I've also heard that they help "deploy" the models into the real world, because the mathematicians and statisticans wouldn't know how to make it public, since that's what a SWE does in normal SWE jobs.

I mentioned potentially doing a stats minor. Is that at all useful? Some courses that I would be taking would be, statistical modeling, probability, regression analysis, analysis of variance and expermentail design, sampling methodoloy, and statistical computing. Maybe I should point out that, I don't want to be really working with a lot of data and graphs and all of that. Hence why I don't want to become a Data Anaylst or Data Scientist for example. I want to code because it's something I enjoy doing, but I want to know if these AI/ML jobs are meant for SWE's but just specific to that field, or are they different in the sense that you need a deeper understanding of math and statistics. If so, how much? And also, if do need higher level of math/statistics, is it like just taking a few more courses, or do you need a Masters/PhD? If it's just a few more courses, does this mean that you're basically just a SWE, and need just some fundamental knowledge to help with your workflow, or it's just completely different?

Essentially, is a stats minor significant in increasing the chances of working in that field? What are the types of tasks you would do in this field, and please if anyone can explain like when you would require higher level of math and statistics versus when you wouldn't like depending on the jobs I would appreciate it a lot. I enjoy math and somewhat statistics, if you were wondering, I'm just trying to figure out what this new field is all about... Thank you so much!

Any help is appreciated, thank you!

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Hey everyone,

I’m looking for a freelance developer (or a small team) to help me build a custom AI-powered tool for business development in the life sciences industry. I work in recruiting and talent solutions, and I need a system that can:

✅ Identify & Track Target Companies – Scrape news and updates (FDA approvals, new executive hires, funding rounds, etc.) to suggest potential target companies based on set parameters. ✅ Research & Summarize Company Information – Automate research on company size, location, leadership, and recent developments. ✅ Generate Draft Emails – Use pre-written templates and personalize outreach based on company news, leadership roles, and past interactions. ✅ Outreach Cadence & Follow-ups – Track outreach schedules, suggest follow-ups, and recommend the best timing for engagement. ✅ Simple UI for Managing Leads – Allow me to review, approve, and manage leads easily with links to LinkedIn, company websites, etc.

I’m not looking for an automated email sender—I want full control over outreach, but I do want AI-powered suggestions to make personalization and follow-up smarter and more efficient.

I’d love to connect with a developer or team who has experience in AI, automation, and web apps. If you’re interested, DM me or drop a comment.

Thanks!

Is it just me, or are most “GenAI Engineers” just prompt engineers using APIs? I always assumed they knew how to build things like LLMs from scratch and the infrastructure surrounding them, but after speaking with numerous GenAI teams it seems that they primarily just use what’s out there already and don’t know the tech behind it. Apologies to those who actually know what they’re doing, but it really seems like the title isn’t as cutting edge as I once thought.

good afternoon

Hope you are well am at my final year of industrial engineering and I would like to ask you the following interesting and scalable ML topics for you, also looking to collaborate with a company or professor to complete an undergraduate thesis linked to ML, this is my last step to finish the degree.

I'm from Chile btw, I speak English too. I hope don't bother anyone

xoxo

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Hi everyone,

I’m currently a PhD student in my last year, specializing in artificial intelligence, particularly computer vision, NLP, and generative AI (think models like Stable Diffusion). I’m based in Spain, and as I approach the end of my PhD, I’m feeling really torn about which career path to pursue: academia or industry.

On the academia side, I love the intellectual freedom, the possibility of pursuing my own research interests, and the opportunity to collaborate with others on cutting-edge projects. That said, I know the pay isn’t great, especially early on: around 26,000 € as an assistant professor, and around 46,000 € as an associate professor. Plus, it’s a long road to stability (if that even comes).

On the industry side, I like the idea of working on applied projects with real-world impact, and the salaries are definitely more appealing. But I worry about losing the creative freedom that academia offers, and I don’t see a lot of AI roles in Spain that truly excite me—they seem to be either very generic or focused on routine work rather than innovative projects.

Here’s a quick summary of the pros and cons I see for each path:

Academia

Pros:

• Intellectual freedom to choose research topics.
• Opportunity to contribute to scientific advancements.
• Flexible work schedules.
• Prestige and fulfillment from mentoring students.

Cons:

• Long and competitive path to a stable position.
• Lower salaries compared to industry.
• Bureaucracy and funding challenges.

Industry

Pros:

• Higher salaries and better financial stability.
• Opportunities to work on real-world, applied problems.
• Structured career progression and skill development.
• Access to better resources and infrastructure for projects.

Cons:

• Less freedom to choose projects; business priorities dominate.
• Potential for long hours and more pressure, depending on the company.
• Some roles can feel repetitive or lack innovation.

I’ve also considered moving to another country, where there seem to be more opportunities for exciting AI work, but I’m not sure if I’m ready for that step yet, as my girlfriend would have trouble finding job opportunities for her career (we engineers have it way easier than other professions).

One of my biggest concerns is that switching paths later might be difficult. For example, returning to academia after earning good money in industry might be challenging, both financially and mentally, as I’d feel like I’d be starting over. On the other hand, going to industry after spending years in academia might feel like losing the academic merits I’ve worked hard to build, especially if I’m starting in a more junior position.

So, I’d love to hear your thoughts:

1. If you’ve faced a similar choice, how did you decide?
2. What do you think are the pros and cons of each path, especially in the context of AI?
3. Are there any hybrid roles that could give me the best of both worlds?
4. If you’re working in AI in Spain (or nearby countries), what’s your experience like? Are there roles that are more exciting than they seem at first glance?

Thanks in advance for your advice! I’m really looking forward to hearing your perspectives.

I am 26 with a 4+1 years degree (in my country these are bachelor plus master programs) in Applied Math and another Masters in Data science. I have been working as a Data Scientist for a risk department for 2 years but now I think I should try my hand in a different field maybe related to Finance or Insurance. Where can I apply my skillset - Great knowledge of Probability and Stats, very good coding skills in Python and SQL with a side of R, grasp of ML algorithms including but now only Regression Analyst, Neural Networks etc, good knowledge of design of statistical experiments.

Are there any other fields that search for a similar skillset?

Hi Everyone,

I have an onsite applied scientist interview with Amazon in the next weeks. Can you please share your experience (regarding the coding, ml or behaviore) with me? I have not talked with recruiter about the details of the interview. Thank you

Hi, are there any machine learning engineers with an interest in computer vision who are VERY skilled at coding?

We have developed a prototype for a medical-based platform but our current dev team doesn't have the technical skills to perfect our platform to make it medical-grade.

We need someone who is SUPER talented and willing to take a risk on a South African Startup. The task is difficult. I'm confident most coders won't be able to do it. It'll make your head spin trying to code it. But if we can get to a perfect (or as close to perfect) model, there is a huge barrier to entry that will likely protect us from much competition

If anyone is interested, please email me: at rushern.r.chetty@gmail.com - we can have discussions - we will give a very fair amount of equity in exchange for services.

Is knowledge of machine learning (ML) becoming increasingly important for software developers, and how can it enhance their skill set and career prospects in the tech industry?

I’m 25f with 6+ years of experience in digital marketing, including freelancing and SaaS startups. I currently work remotely in marketing and love the flexibility, but I’m considering a career shift to Machine Learning or Data Science for better long-term prospects.

I’m a complete beginner in coding and math (other than basic data cleaning on Kaggle) and would need to start from scratch. Are there any online courses or training programs you’d recommend for someone new to this field?

Also, how realistic is it to break into the field as a beginner? What’s the best way to secure a first job in Machine Learning or Data Science?

Any advice would be hugely appreciated—thanks in advance!

I looked into our OmniJobs database and gathered list of latest remote ML jobs. I hope this helps you on the job search.

• Senior Software Engineer @ Acorns

• Engineering Manager, AI @ Lattice

  • 💰 $188–235K/y

• Quantum Life Sciences Chemist @ Current Job Openings

• Remote-Senior Security Automation Engineer @ SimSpace

  • 💰 $115–165K/y

• Senior Software Developer In Test, Caper @ Instacart

• Senior Full Stack Engineer, Ingress @ Rocket Money

  • 💰 $150–185K/y

• Senior Software Engineer, Data - AI Engine @ Regrello

  • 💰 $165–220K/y

• Engineering Manager, Data Platform @ Arize AI

  • 💰 $180–250K/y

• Principal, Machine Learning Engineer @ Twilio

  • 💰 $200–250K/y

• ServiceNow Full Stack Developer (Nightshift) @ rockITdata

• Full Stack Engineer (React/Python) @ Literati

  • 💰 $140–150K/y

• Java Staff Software Engineer/Tech Lead, iQueue for Operating Rooms (US Remote) @ LeanTaaS

  • 💰 $140–190K/y

• Software Engineer, Machine Learning @ Serve Robotics

• Software Engineer 3 @ eBay Engineering&Research

  • 💰 $157–206K/y

• Senior Manager, Software Engineering @ Socure Inc.

  • 💰 $190–220K/y

• Software Engineer @ Helm.ai

• Senior Engineer - VSLAM @ Motional

  • 💰 $147–225K/y

• Senior Software Engineer @ Chameleon Consulting Group

• Principal Solution Technical Architect, ServiceNow @ RIMINI_USA Rimini Street

• Machine Learning Architect (AWS) @ Rackspace

  • 💰 $153–245K/y

• Software Engineer - Backend @ Evolent Health International Private Limited

• Software Engineer, Senior @ Xator LLC

  • 💰 $117–211K/y

• Machine Learning Specialist (Platform) @ USA 3M Healthcare US Opco LLC

  • 💰 $137–168K/y

• AI Safety Engineer (Back-End) @ ElevenLabs

• Product Engineer (MLOps) @ Swish Analytics

• Machine Learning (Pre-Sales) Solutions Engineer @ Snorkel AI

• Senior Machine Learning Engineer @ Redesign Health

• Associate Director, Global Solutions @ IPG Mediabrands

  • 💰 $120–130K/y

• AI Software Solutions Engineer @ Intel Semiconductor Storage Technology (Dalian) Ltd.

• Senior Manager, Software Engineering and AI @ Calix North America

  • 💰 $163–277K/y

• Senior Full Stack Engineer, Investigator @ Job Board

• Senior Frontend Software Engineer - Checkout (100% remote) @ Hopper

• Software Engineer, ML & Search Infrastructure (multiple levels) @ Reddit

  • 💰 $186–322K/y

• Senior Manager, ML Platform @ Stitch Fix

  • 💰 $178–262K/y

• Staff Software Engineer, Backend (ML Platform) @ Affirm

  • 💰 $200–250K/y

Like the post if you found this useful :)

Three of us have just been through all the stages of the xAI AI Tutor application process, and rejected after the interview, which in all cases went well and we were each lead to believe/told we'd be receiving offer emails. We're all highly qualified and capable, none of us can understand why we were rejected, and we're pretty pissed about it.

So, who else could we apply to, that won't waste our time like xAI? We're in the UK. We'd consider full and part time roles.

Hi everyone,
I need your help with some data related courses recommendation,

I'm marketing analyst at my company and they quite like me, this is my first job and my company offered to sponsor any relevant courses to my position (I tried to stretch it to pay for my part time masters, but they did not agree)
I did mathematics and with that I did some python/data analytics modules at university. I do not have enough knowledge in this field but I do a lot of stuff in excel or some basic commands in python so the results run faster. I also use all of the typical marketing analysis software like google analytics, looker studio or salesforce, for internal company data.

I am not sure marketing world is for me and I want to do data science masters at some point.
I would love those recommendations to be able to 1) make me better at my job, 2) look good on uni application 3) land me a position in maybe a little bit more serious data field (ideally, better paying one as well).

I will appreciate all of the help!
I have no knowledge what is recommended and beneficial, so I'm super curious what you think. I'm super flexible so I can do both online or hybrid courses. I would love to do some classes, or courses which require me to even sync with a tutor or a group, I'm London based, so if you even have in person reccs that would be great.

I'm currently a researcher with a good stipend, but I'm looking to transition into the field of machine learning and deep learning. However, I've noticed that remote opportunities in this field are extremely limited, and the salary packages offered by companies in this domain are often quite low compared to my current earnings.
Additionally, I live in a less developed city where there are very few job opportunities in tech-related fields. Moving to another city for better prospects seems challenging due to the higher cost of living and other lifestyle adjustments.
Have you ever been in a similar situation? How did you handle it? Any advice on how to navigate this transition or make the right decision?