Ask HN: How to Break into AI Engineering

I'm definitely not any authority on the matter -- also self taught, but I don't think so.

Having a conceptual understanding of the math is probably a really good idea, but I imagine there's quite a bit that can be hidden behind an api that you can essentially say "does xyz by means of magic".

I imagine this winds up creating three distinct roles: - Focusing on improving the model's usefulness - Focusing on implementing the model for optimal performance - Focusing on using the model within a larger application

I say "creating", but I imagine this is also probably roughly the current status quo.

I work in machine learning and this isn't how I see it at all. Data engineering specifically evolved as a term to differentiate the people who work with data but don't work on ML/AI.

Edit: I didn't have the laws/properties of logarithms memorized/understood until maybe 3-4 years into my Math degree. I could have learned it sooner probably, but I just had an aversion to it and would desperately translate any problem into exponentials and work with those instead. I definitely sympathise with the desire to "restart" Math.

1. MLE/DE/MLOps - this is more like typical software engineering. You're responsible for building data platforms, tools, monitoring, and more around the model development lifecycle. This can include: data ingestion, data architecture, data transformation and storage, automating and productionizing various workflows like training, evaluation, and deployment, monitoring deployed models, data monitoring (and building monitoring), tooling like feature stores (and libraries for R&D teams to interact with them) or internal deep learning frameworks, etc. You'll basically work as a part (or an adjunct to) the research team that is testing new model architectures, different approaches towards some goal, etc. These are largely taken from my own experiences and projects I've built. Skills: software engineering, Python, knowledge of the model development lifecycle, data architecture/engineering, some knowledge about the frameworks used, cloud platforms, etc. Designing ML Systems by Chip Huyen is a great overview of all of this kind of work.

2. Research. This is actually building models, implementing papers, very occasionally (especially in big companies) doing publishable research. This is more akin to academic work (my educational background is in hard science academia), and requires a lot of paper reading, experimentation, etc. It will require knowledge of your niche (I mostly work with CV teams, for instance), strong math fundamentals, and very often a PhD.

I can tell you how I, as a self-taught software engineer with a bio education got here. My first job was a generic enterprise desktop application development role, randomly joined a data engineering team shortly after that not even knowing what DE was, but knowing I liked it. We worked on a massive distributed ETL system. I then joined my first startup, it was also a DE role, but we were a small group in a larger research team where I got my initial exposure to ML workflows and especially moving them to the cloud. We did some simple model training, data management, and building products around the models we built while also supporting the research efforts of the larger team.

I then went to another startup, where I had the sole responsibility of our research infrastructure (largely based on the strength of my knowledge of AWS and Python). I was the sole engineer on a team of CV researchers, and did things like automate their entire evaluation workflow and move it to the cloud, worked on the internal deep learning framework, and built a team to evaluate the current AI development lifecycle and design a platform to harden and optimize the process. Covid put the kibosh on that. I moved to another, earlier startup, doing similar work but more foundational - almost everything was built from scratch.