AI/ML Engineer Career Guide: Salary, Skills & Job Outlook

AI/ML engineers build the systems that learn from data and make predictions or decisions without explicit programming. That sounds abstract, so here's what it looks like in practice.

You might spend your morning training a recommendation model that decides what content users see on a social media platform. After lunch, you're debugging why the model performs worse for Spanish-language content than English. By 3pm, you're in a meeting arguing about whether to retrain the whole model or fine-tune it on more Spanish data. Then you spend the evening writing a pipeline to automate the data preprocessing so you don't have to do it manually next time.

The work splits into a few broad categories. Some ML engineers focus on research, developing new algorithms and pushing the boundaries of what's possible. Others focus on production ML, taking models that work in notebooks and making them work reliably at scale. And a growing number specialize in MLOps, building the infrastructure and tooling that lets other ML engineers deploy and monitor models.

One thing that surprises a lot of people: ML engineers spend more time on data and infrastructure than on model architecture. A widely cited figure in the ML community says that 80% of the work is data preparation and only 20% is actual model development. Clean, well-structured data matters more than clever algorithms in most production systems.

AI/ML engineers are among the highest-paid professionals in tech. The average AI engineer salary hit $206,000 in 2025, a $50,000 jump from the year before. That's not a typo. The talent shortage is that severe.

ML engineers earn roughly 67% more than general software engineers, according to Signify Technology's 2025-2026 salary benchmarks. And at top companies, the numbers go much higher. Levels.fyi reports a median total compensation of $260,750 for ML engineers across major tech firms.

The BLS doesn't have a dedicated category for AI/ML engineers. The closest match is Computer and Information Research Scientists (SOC 15-1221), with a median salary of $145,080 as of 2024. But that category is broader than ML engineering and includes non-ML research roles, so it understates what most ML engineers actually earn.

The reason for the pay premium is simple: there are about 1.6 million open AI/ML positions and only about 518,000 qualified candidates. A 3.2-to-1 demand-to-supply ratio means companies have to pay up to attract and keep ML talent.

Experience drives salary more than anything else in ML engineering. Here's how compensation breaks down by level, based on aggregated data from Signify Technology, PayScale, and Glassdoor.

Entry-level AI/ML engineers (0-2 years) earn between $98,000 and $148,000 in base salary, with a typical midpoint around $120,000. That's significantly higher than entry-level software engineering, and it reflects the specialized education most ML roles require.

Mid-level ML engineers (3-5 years) see a big jump to $149,000-$200,000, with a midpoint around $175,000. Mid-level ML talent saw 9% year-over-year salary growth in the latest benchmarks. The jump from junior to mid-level is about 29%.

Senior ML engineers (6-10 years) earn $175,000-$275,000 in base, with a midpoint around $220,000. At this level you're designing ML systems end-to-end and owning critical production models.

Staff and principal ML engineers (10+ years) earn $235,000-$355,000 in base salary, with a midpoint around $290,000. Total compensation at top companies can reach $500,000-$700,000 or more.

Two factors that push salaries even higher: a PhD adds a 15-30% premium ($21,000-$55,000 above base). And specializing in generative AI or LLMs adds a 40-60% premium above baseline ML salaries. If you have a PhD and GenAI expertise, you're in a very strong negotiating position.

AI/ML engineering jobs are concentrated in a few markets, and the pay differences between them are significant.

The San Francisco Bay Area leads, unsurprisingly. Senior ML engineers in SF earn $220,000-$275,000 in base salary, and lead/principal roles reach $260,000-$355,000. California holds 29% of all AI/ML jobs in the U.S.

New York City comes second, with senior ML engineers earning $200,000-$250,000 and leads hitting $240,000-$320,000. New York holds 17% of AI/ML jobs nationally.

Seattle is a strong third option, with senior salaries of $195,000-$245,000. And like we noted in our software engineer career guide, Washington's lack of state income tax means you keep significantly more. An ML engineer earning $200,000 in Seattle takes home roughly $15,000-$25,000 more per year than someone earning the same in San Francisco.

Austin is emerging as an AI hub, with senior salaries of $175,000-$225,000. Lower cost of living makes this stretch further than coastal equivalents.

By state, ZipRecruiter data puts Washington at the top ($175,132 average), followed by California ($171,872) and New York ($151,101). Florida pays the least among major tech states.

Remote ML engineer positions typically pay $105,000-$295,000 depending on experience. Many companies adjust for location, but the talent shortage gives remote ML engineers more leverage to negotiate location-agnostic pay than most other roles.

Not all ML engineers are interchangeable. Your specialization within AI/ML has a major impact on what you earn. Based on data from Glassdoor and ZipRecruiter:

• ML Research Scientist: $226,353 average (ZipRecruiter), ranging from $179,000-$360,000. The highest-paying ML specialization, but almost always requires a PhD
• AI Research Scientist: $194,572 average, ranging from $158,000-$293,000. Top lab offers (Google Brain, DeepMind, OpenAI) can hit $550,000+ in total comp for new PhD grads
• Machine Learning Engineer (general): $186,146 average (Indeed), ranging from $122,000-$265,000. The broadest category
• Data Scientist (ML-focused): $170,623 average (Glassdoor), with ML focus adding a significant premium over the BLS median of $112,590 for all data scientists
• Computer Vision Engineer: $162,335 average, ranging from $128,000-$259,000. Strong demand in autonomous vehicles and robotics
• NLP Engineer: $161,295 average, ranging from $134,000-$196,000. NLP appears in 19.7% of job postings, driven by the LLM boom
• MLOps Engineer: $161,317 average, ranging from $132,000-$257,000. Growing rapidly as companies realize deploying models is harder than building them

The biggest salary premiums within ML right now: GenAI/LLM expertise adds 40-60% ($56,000-$110,000), MLOps skills add 25-40% ($35,000-$74,000), NLP adds 20-35%, and Rust for ML adds 15-20%. PyTorch proficiency alone is worth an 8-12% premium.

The companies paying the most for ML talent are a mix of big tech and finance. Levels.fyi data for ML engineers specifically:

• Apple: $373,000 median total compensation for ML engineers
• Google: $344,000 median TC, ranging from $199,000 to $743,000+
• Meta: $302,000 median TC, ranging from $187,000 to $785,000+
• Snap: up to $806,000+ total comp at senior levels
• Amazon: $265,000 median TC, ranging from $176,000 to $401,000+

Other major ML employers include OpenAI, NVIDIA, Microsoft, Netflix, Spotify, Adobe, TikTok, and Pinterest. NVIDIA in particular has become a critical employer in the ML space as their hardware powers most model training.

By industry, hedge funds and quantitative trading firms pay the most: $300,000-$500,000+ at the senior level, and up to $1 million+ for leads. Big tech comes second at $320,000-$550,000 for senior ML engineers. AI-first startups offer $240,000-$400,000 at senior levels, often with meaningful equity. Healthcare and pharma are growing sectors for ML, with senior roles paying $220,000-$340,000.

A notable shift: more than 50% of AI jobs in 2025 are now outside traditional tech. Marketing, healthcare, finance, manufacturing, and retail are all hiring ML engineers. The field isn't just a Big Tech game anymore.

The education bar for AI/ML engineering is higher than general software engineering. Most job descriptions ask for a master's degree, and many research roles require a PhD. That said, there are paths in without a graduate degree.

Common educational backgrounds for ML engineers: computer science (most common), mathematics/statistics, electrical engineering, physics, and data science. The math matters more here than in general software engineering. You'll struggle without solid foundations in linear algebra, calculus, probability, and statistics.

Based on Axial Search's analysis of AI/ML job postings and industry surveys, here's what employers are looking for in 2026.

An emerging trend: Rust for ML. It's still niche, but engineers with Rust skills for ML inference and systems work earn a 15-20% premium. The ML ecosystem is slowly moving beyond Python for performance-critical components.

ML engineering has a career ladder similar to software engineering, but with a stronger emphasis on research and domain expertise at senior levels.

One important note: 78% of AI/ML job postings target professionals with 5+ years of experience, and 57.7% prefer domain experts over generalists. This is a field that rewards deep expertise over breadth.

The numbers are striking. The BLS projects 26% growth for computer and information research scientists from 2024 to 2034, far above the national average. For data scientists (a related role), the projection is even higher at 34%, making it the 4th fastest-growing occupation in the entire U.S. economy.

AI/ML job postings hit 49,200 in 2025, up 163% from 2024. The ML market is projected to reach $503.4 billion by 2030, up from $113.1 billion in 2025. Global spending on AI systems is expected to exceed $300 billion by 2026.

The World Economic Forum projects 170 million new AI-related jobs globally by 2030. Even accounting for 92 million jobs displaced, that's a net gain of 78 million positions. AI and big data top the WEF's list of fastest-growing skills worldwide.

The biggest driver: 86% of employers expect AI to transform their business by 2030. Every industry is trying to adopt ML, and there aren't enough ML engineers to go around. That 3.2-to-1 demand-to-supply ratio shows no signs of closing.

The role itself is evolving. Increased demand for engineers who can deploy, fine-tune, and manage LLMs is reshaping what ML engineer job postings look like. The field is shifting toward more MLOps, deployment, and production-system design versus pure model development. If you can both build models and deploy them reliably at scale, you're extremely valuable.

If any field in tech is "safe" from AI automation, it's the field of building AI itself. ML engineers who understand both the theory and the engineering are in the strongest position of anyone in the tech job market right now.