Machine Learning Jobs Analysis: A Data-Driven Look at 1,000 Machine Learning Job Postings in 2025
Joy·
Over the past year, the world has witnessed a generative AI boom unlike anything before. Tools like ChatGPT, Midjourney, and Claude have transformed how we write, code, design, and even think. Behind these breakthroughs lie massive language models and relentless advancements in machine learning. As GenAI continues to evolve at breakneck speed, companies across every industry are racing to keep up — not just by adopting these technologies, but by hiring the talent that can build and scale them.
So what does that hiring surge actually look like?
To answer that, we analyzed a dataset of 1,000 machine learning–related job postings collected from company career sites and job boards across the United States, spanning from late 2024 to early 2025. From the rise of GenAI engineers to the continued demand for traditional ML roles, this dataset offers a snapshot of where the industry is heading — and who’s driving the change.
Let’s dive into the numbers.
The dataset used in this blog is downloaded from Kaggle. The analysis is powered by Powerdrill.
Curious where machine learning engineers are landing the most job opportunities? We broke down the top 10 U.S. cities and states hiring for ML roles — and the results are no surprise.
At the top of the list is San Francisco, CA, with 105 job postings, followed closely by Los Angeles, CA, which boasts 90 open roles. The rest of the leaderboard is packed with tech hotspots like:
Menlo Park, CA – 39 postings
San Jose, CA – 33
Seattle, WA – 33
San Francisco (additional listings) – 31
New York, NY – 28
Mountain View, CA – 25
Santa Monica, CA – 19
Boston, MA – 17
California dominates the ML hiring landscape, with both San Francisco and Los Angeles leading the charge.
The Bay Area (Menlo Park, San Jose, Mountain View, and surrounding cities) continues to thrive as a major hub for machine learning talent.
Beyond California, Seattle, New York, and Boston also show strong demand, highlighting a nationwide push for AI expertise.
ML hiring is booming. Looking at the data, one trend is crystal clear: the real hiring boom didn’t start until early 2025. From December 2022 through most of 2024, job postings remained relatively quiet — with few signs of large-scale growth. But that calm didn’t last.
In March 2025, demand suddenly spiked, with 425 job postings, followed closely by 433 in April — a dramatic leap compared to previous months.
The timing aligns closely with the acceleration of GenAI adoption and enterprise investments in large language model infrastructure. Companies aren’t just experimenting anymore — they’re hiring real teams to build, integrate, and scale.
The surge in machine learning roles isn’t just about where — it’s also about who is hiring. Our analysis of job postings reveals a competitive field, but a few big players are clearly leading the charge.
The surge in machine learning roles isn’t just about where — it’s also about who is hiring. Our analysis of job postings reveals a competitive field, but a few big players are clearly leading the charge.
At the top is TikTok, which stands out with a commanding 88 ML job openings — more than double any other company on the list. Meta comes in second with 39 postings, reinforcing its deep investment in AI infrastructure and research.
Other notable companies include:
Snap Inc., Adobe, and Splunk – 18 postings each
Netflix and DoorDash – 17 each
Amazon – 15
AWS (Amazon Web Services) – 13
Slack and Waymo – 11 each
TikTok and Meta are clearly dominating the ML hiring landscape, indicating aggressive expansion in AI and GenAI initiatives.
A diverse mix of tech players — from streaming (Netflix) to logistics (DoorDash) to enterprise tools (Slack) — are actively seeking ML talent.
The long tail of companies with smaller but steady hiring volumes reflects how widespread machine learning needs have become, even beyond traditional “AI-first” firms.
It’s no longer just Big Tech chasing ML engineers — it’s nearly everyone.
Job titles reveal more than just roles — they offer a glimpse into how companies are positioning their AI teams. In this dataset, one title clearly stands above the rest.
Machine Learning-related roles dominate the field, appearing 786 times. This includes any title containing “machine learning” or “ML,” highlighting the central focus on this discipline.
Data Scientist comes in as the second most common title, with 116 listings — still significant, but far behind the ML category.
The “Other” category, which includes all titles that don’t fit neatly into predefined buckets, appears 74 times.
Roles that explicitly mention “AI” appear 20 times, suggesting a more general or conceptual framing.
Applied Scientist is the rarest in the dataset, with just 1 appearance — indicating it’s likely used more narrowly or within specific orgs (e.g., Amazon, Microsoft).
“Machine Learning” is the title of choice for employers, reflecting a desire for deep specialization rather than generalist AI talent.
Data Scientist roles remain highly relevant, especially in organizations focused on data-driven product insights.
“AI” and “Applied Scientist” titles are far less common, pointing to more niche hiring strategies or role definitions within specific tech ecosystems.
As GenAI reshapes team structures, the title “Machine Learning Engineer” isn’t just popular — it’s becoming the new standard.
When it comes to machine learning roles, not all job levels are created equal. Our analysis reveals a clear trend: companies are primarily hiring for mid-level and entry-level talent — with senior leadership positions far less common.
Here’s how the numbers break down:
Mid-Senior level roles appear most frequently, with 371 listings — suggesting that employers are looking for engineers with a few years of hands-on experience who can contribute immediately.
Entry-level positions follow closely behind, with 300 postings, offering strong opportunities for newcomers and recent grads to break into the field.
Not Applicable shows up 209 times, often representing postings where seniority wasn’t specified or didn't fit into standard categories.
Internships also appear in decent numbers — 70 listings, reflecting a healthy pipeline of junior talent development.
Associate roles show up 32 times, often indicating early-career or transition-level jobs.
Director-level roles are rare, with just 5 postings, and executive roles are nearly nonexistent — with only 1 listingin the entire dataset.
Mid-Senior level is the hiring sweet spot, showing strong demand for professionals with 3–5 years of ML experience.
Entry-level hiring is robust, suggesting that many teams are open to training and growing new talent.
Top-level leadership roles in ML are scarce, likely reflecting that AI leadership is still centralized within a small number of roles or senior specialists.
For those entering the workforce or looking to move into a more hands-on ML role, the market looks wide open — as long as you’ve got the skills to match.
When it comes to machine learning job descriptions, the tools you know can make or break your chances. Our analysis of 1,000 postings reveals which skills are truly in demand — and which ones are still gaining traction.
Here’s what we found:
Python reigns supreme, showing up in a massive 752 job descriptions — cementing its role as the universal language of machine learning.
AWS appears in 493 listings, confirming that cloud-based infrastructure is now a baseline expectation.
PyTorch and TensorFlow, the two leading deep learning frameworks, are mentioned 469 and 388 timesrespectively — showing near-equal footing in real-world job requirements.
SQL appears in 294 postings, reflecting its continued relevance in data querying and preprocessing workflows.
LLMs (Large Language Models) are found in 206 listings, a strong signal that GenAI is making its way into mainstream hiring needs.
MLOps shows up in 142 job descriptions, making it the least frequently mentioned — but still notable, especially as more companies focus on scaling and productionizing ML pipelines.
Python is non-negotiable. If you're working in ML, you need to be fluent.
Cloud fluency (especially in AWS) is just as important, as companies look to deploy models at scale.
Both PyTorch and TensorFlow remain core competencies, with no clear "winner" — knowing both is a bonus.
LLMs and MLOps are rising, but haven't yet reached the ubiquity of more mature tools. That said, they’re strong differentiators if you're targeting cutting-edge GenAI teams.
Whether you're a beginner or a seasoned ML engineer, aligning your skill set with these in-demand technologies could be the key to unlocking your next opportunity.
Job titles offer a window into how companies structure their AI teams — and what they’re really hiring for. To find out what roles are most in demand, we analyzed the top 10 most frequently listed job titles across 1,000 machine learning-related job postings.
The numbers speak for themselves:
Machine Learning Engineer leads by a huge margin, appearing 243 times, making it the clear front-runner in ML hiring.
Data Scientist comes in second, with 53 listings — still significant, but far behind the top spot.
Other frequently used titles include:
Software Engineer, Machine Learning – 30
Senior Machine Learning Engineer – 22
Software Engineer, Machine Learning (Multiple Levels) – Slack – 9
Machine Learning Engineer, AI (Fully Remote, USA) – 9
Machine Learning Engineer, AI Platform (Fully Remote, USA Only) – 8
Machine Learning Engineer II – 8
Artificial Intelligence / Data Scientist Intern (HR) – 8
Software Engineer – 7
"Machine Learning Engineer" has become the industry’s go-to title, reflecting both specialization and maturity in how companies define this role.
"Data Scientist" remains relevant, though it's increasingly positioned differently from more engineering-focused ML roles.
The long tail of titles (including variants like “ML Engineer II” or “AI Platform”) suggests teams are experimenting with role naming to reflect level, focus, and remote flexibility.
In short, if you're applying for jobs in ML, chances are your dream role starts with “Machine Learning Engineer.”
From booming demand in early 2025 to the dominance of titles like Machine Learning Engineer and the clear preference for skills like Python, AWS, and PyTorch — this dataset reveals a job market that’s rapidly evolving alongside breakthroughs in GenAI and large language models.
California remains the epicenter, with cities like San Francisco, Los Angeles, and Menlo Park leading hiring efforts. But the need for ML talent is clearly spreading, with companies across industries — from tech giants like TikTok and Meta to startups and remote-first teams — aggressively growing their AI capabilities.
Meanwhile, hiring trends show a strong focus on mid-level and entry-level roles, with relatively few senior executive postings. That’s a signal: companies are looking to build deep technical teams from the ground up.
Whether you're an aspiring ML engineer or a company looking to stay ahead in the AI race, this snapshot of 1,000 job postings is a reminder that the machine learning job market isn’t just active — it’s accelerating. And the skills, titles, and locations in demand today will shape where the industry goes tomorrow.