Hi everyone! I’ve found job hunting to be especially difficult for AI roles right now even though the job listings for AI roles are only rising. Here are my thoughts on it and some interesting roles I’ve found as I’ve scanned listings recently. Let me know if you enjoy posts about finding jobs and I can do these more frequently and answer any pressing questions.
Always be (machine) learning,
Logan
I’ve been scanning job listings to better understand the job market for the past few months and want to more regularly share my findings with you. The good news: AI listings are on the rise and most of those listings are looking for software engineers. Those jobs also pay very well.
The bad news: Most of these job listings are terrible. They don’t properly explain the role, don’t have the right requirements, or are looking for a ridiculous number of years of experience. They frequently use overloaded or vague terminology (i.e. “Machine Learning Engineer” without an explanation of what the role actually does)1. All of this combines for an abysmal job hunting experience.
This past week the most common technical requirement for AI jobs was Python. This makes a lot sense since Python is the language of machine learning and they’d want whoever they’re hiring to know it. The next three technical requirements are: Machine Learning, AI, and ML.
Not only are two of those requirements the same thing but all three of them are very vague. A technical requirement of “machine learning” or “AI” could me many things. This language is also used in the role description making it can be almost impossible to decipher what a company is looking for in an applicant.
This has two major impacts on job hunting:
Engineers looking for AI roles don’t even know if they qualify. Engineers with experience in DevOps and an understanding of machine learning will assume they don’t qualify for what could be an MLOps role if only “machine learning” is listed in the job skills.
Engineers don’t even know what to gain experience in to qualify for these roles at a later date. A listing that specifies “transformers” or “LLM evaluation” or even the type of machine learning algorithm a candidate will be working with gives a candidate a topic to learn more about. A listing that says “AI” gives them nothing.
Companies also seem to default job requirements to 5+ years applying machine learning in an industrial setting. I see this same value across many companies. Unfortunately, 5+ years ago there weren’t many opportunities to work on machine learning in industry. This means very few individuals meet this criteria even though many individuals would likely thrive in the role.
The main takeaway here is not to be discouraged by job listings. AI needs software engineers and there are many opportunities opening up that pay very well. In the future, it likely won’t be a choice for software engineers to be familiar with AI because it’ll be used everywhere. But for now, it’s a really good idea to become familiar with it and move into a role in AI if it interests you. If you’re interested, I’ve put together a roadmap to help you learn AI for free.
If you’re interested in AI engineering, don’t forget to subscriber to articles like this in your inbox each week. Don’t forget to leave a comment if you find this article to be helpful so I know to write more of them. Let’s get into the roles!
Interesting Roles
Here’s a quick overview of how the industry looks:
Most common experience requirement: 5+ years
Top technical skills: Python, PyTorch, Tensorflow, Transformers, C++
Educational requirements (or equivalent experience needed): Primarily master’s, some bachelor’s, and fewer PhD requirements.
Average pay: $150k-$200k all the way up to $720k (based on listings that included it)
I’ve found there to be about 5x as many full-time roles as there are internships. Internships generally require PhD enrollment.
Recent grad positions are almost immediately filled.
Reminder: These roles may be filled between the time I write this and you view it.
Full-time
Netflix: Machine Learning Engineer on the algorithms team developing and scaling ML solutions for personalization, recommendation, and user experience. Requires 5+ years of experience and varied skills such as Python, C++, recommendation ML algorithms, and personalization ML algorithms. More info at the 4 job listings here, here, here, and here. Netflix pays well and is remote friendly.
Notion: Software Engineer, Machine Learning working on productionizing AI and LLMs into Notion’s products. 3+ years of experience is required along with skills in natural language processing, building LLM-powered products, and embeddings. More info in the job listings here, here, here, here, here, and here. Jobs are in New York, NY and San Francisco, CA.
Waymo: Machine Learning Engineer working on training infrastructure. These positions require 2+ years of experience along with skills in Python, C++, and PyTorch or Tensorflow. More info here and here. Jobs are located in Mountain View, CA.
LinkedIn: AI Engineer working on algorithms to personalize the user experience. Requires 1+ year(!!!) of experience along with skills in a widely used programming language and software system design. More info here. The job is located in Sunnyvale, CA.
Internships
Netflix: Machine Learning Engineer Intern for Summer 2025 working on a variety of things (seems they aren’t specific about what interns will work on). Requires a current PhD enrollment and experience with Python. More info can be found here, here, here, and here. The job is located in Los Gatos, CA.
Bose Professional: Software Engineer Co-op in AI working on the analysis of audio software and training machine learning models. Requires experience with Python, Tensorflow, PyTorch, and NumPy. Suitable for a Master’s student for junior/senior in undergrad. More info here, here, and here. Jobs are located in Hopkinton, MA.
Let me know if any links are incorrect or dead. Also, let me know of any improvements that can be made to make this information more accessible to you.
A machine learning engineer is any software engineer building systems that are impacted by machine learning. There are many differences between ML and traditional software systems and the engineer must understand what those are and build accordingly. I’ve seen this title used for anything from research, to MLOps, to building with an ML API. Machine learning engineer can be a very large umbrella.
