ML Engineer Salary: US Rates, Levels, ML Engineer vs AI Engineer

Accurate ML engineer salary benchmarks help you set a realistic hiring budget and avoid costly adjustments during recruitment. Growing demand for AI expertise has put pressure on compensation, so salary figures from previous years may no longer reflect what it takes to hire qualified ML engineers in 2026.

This article walks through current ML engineer pay figures by experience level, US market, and skill set, and compares pay to the AI engineer role. Use it to benchmark a hire, set a budget, or check where your own offer stands.

What is the Average Salary for a Machine Learning Engineer in 2026?

The average base salary for a machine learning engineer in the US is $162,080, according to Built In. With bonuses and equity, average total pay reaches $212,022. For employers, that adds nearly $50,000 to the annual compensation estimate for one hire, before other overhead costs.

Across major aggregators, ML engineer salary data varies:

These figures differ because each source uses its own methodology and draws on a distinct group of respondents. When planning a hiring budget, use the reported salaries as a range and check which experience levels and compensation components each source measures.

Demand context matters here. McKinsey’s Technology Trends Outlook 2025 found that employers had fewer available workers with machine learning skills than they needed. The talent pool covered 90% of estimated demand, leaving a gap for companies looking to hire these specialists. To attract qualified candidates, employers competing for the same specialists need to offer market-rate salaries and competitive total compensation.

ML Engineer Salary by Experience Level

Pay varies most by experience level. At leading employers, the gap between entry-level and staff-level total compensation tops $150,000.

Entry-Level Machine Learning Engineer Salary

The average entry-level ML engineer pay in the US is $69,362 a year, according to ZipRecruiter. Most salaries fall between $51,500 and $78,500, while top earners make $102,500 or more. These figures apply to roles with fewer than two years of experience and typically exclude significant equity.

Employers hiring entry-level ML engineers typically look for a strong foundation in Python and familiarity with frameworks such as TensorFlow or PyTorch. Candidates may have a relevant master’s degree, project experience, or both. Since entry-level hires often have limited production experience, employers should assess their understanding of ML fundamentals and ability to apply them to project tasks.

Mid-Level Machine Learning Engineer Salary

Mid-level engineers with three to five years of experience typically earn between $101,000 and $178,000 in base salary. At this stage, engineers own the model pipeline, manage data preprocessing, and weigh in on architecture decisions.

Equity can add substantially to annual compensation. Pave’s 2025 AI & ML compensation report identifies a premium for AI/ML talent in annualized new-hire equity grants and notes that public companies also use equity to attract specialists.

Senior Machine Learning Engineer Salary

The senior machine learning engineer salary ranges from $129,000 to $193,000 per year, according to Glassdoor. At the staff level, base salaries can reach $230,000 to $310,000 at top-paying employers.

Senior ML engineers design system architecture, lead projects across teams, and mentor junior specialists. At FAANG companies and frontier AI labs, stock awards and bonuses can push total compensation above $350,000 a year.

ML Engineer Salary by Location

ML engineer salaries differ by location, partly because employers compete for the same specialists. Mercer’s geographic pay research found that San Francisco pay rose from 19% to 26% above the national average over four years. Competition from major AI and technology employers, including Google, OpenAI, Anthropic, and Meta, can push offers even higher for in-demand ML specialists.

Pave’s compensation analysis groups San Francisco, New York City, and Seattle among the highest-paying US markets for software engineers. Austin, Boston, and Chicago fall into the next tier, where base salaries average 11% below those in the top-paying markets.

Employers also use different pay policies for remote ML engineers. Pave’s research on geographic pay differentials explains that employers may set remote employees’ pay based on their home location, the nearest office, or a national pay rate. As a result, two ML engineers in the same role may receive different offers depending on where they live and which policy their employer follows.

For US companies facing high local salary benchmarks, Geomotiv offers another way to access ML expertise. Through staff augmentation or dedicated team models, you pay an agreed-upon hourly or monthly rate, without the payroll costs of hiring full-time staff. Geomotiv’s engineers work under our management, so your team can access ML engineers at competitive rates.

How Skills and Specialization Affect ML Engineer Pay

Beyond seniority and location, specialization also affects ML engineer salaries. You may need to pay more for specialists with specific skills, even when they have similar experience levels.

The highest-compensating specializations in 2026 include:

• LLM development. Roles that require hands-on LLM training, fine-tuning, and RLHF experience may need a higher salary budget than positions focused on more conventional ML work.
• MLOps and production infrastructure. Engineers who build and maintain reliable ML pipelines are in short supply, which adds $15,000 to $25,000 to typical benchmarks for this skill.
• Reinforcement learning (RL). Companies developing robotics, gaming, and autonomous systems need experienced RL engineers. If your project depends on these skills, budget for the specialized experience the role requires.
• Recommendation systems. eCommerce businesses pay a premium for engineers who have shipped large-scale recommendation models in production.
• Computer vision. Healthcare, automotive, and manufacturing companies compete with tech employers for computer vision specialists, which can drive salaries above general ML engineer pay benchmarks.

Certifications can help you assess engineering background, but they rarely tell the whole story. For hiring decisions, give more weight to production experience, deployed models, and contributions to real projects.

ML Engineer vs. AI Engineer Salary: Key Differences

Although the ML and AI engineering roles overlap, their core responsibilities can affect candidate requirements and salary budgets.

Machine learning engineers focus on developing, training, evaluating, and deploying ML models. AI engineers focus on building applications that use AI capabilities. They often integrate existing models through APIs, develop retrieval-augmented generation (RAG) systems, and connect AI components to enterprise software. Tech companies and startups may offer AI engineers higher base salaries because they require engineers who can turn AI capabilities into working product features and integrate them into existing systems.

If your project requires custom model development or optimization, an ML engineer may be a better fit. When you need to integrate existing models into a product or business workflow, an AI engineer can better match your requirements. Organizations investing in end-to-end AI and ML development may need both profiles: ML engineers to build and maintain model infrastructure, and AI engineers to ship product features on top of it.

Total Compensation Package: Beyond Base Salary

Base salary is only one part of an ML engineer’s total pay. Equity, cash bonuses, and benefits can increase the annual cost of a new full-time hire, particularly for senior specialists.

When setting a hiring budget, account for the following components:

• Equity grants. RSUs at public companies typically vest over four years with a one-year cliff. At some leading technology companies, annual equity value for senior engineers exceeds $100,000.
• Annual performance bonus. Bonuses at established technology companies often range from 10% to 20% of base salary. The final payout depends on individual performance ratings and the company’s financial results.
• Sign-on bonus. Companies may offer a one-time payment to attract candidates who would lose unvested stock awards by leaving their current employer. At top companies like Google, sign-on bonuses can range from $30,000 to $80,000 and $100,000+ for senior engineers.
• Remote work stipends. Home office allowances and internet reimbursements add $1,000 to $3,000 a year at many leading technology employers.
• Learning and development budgets. Conference fees, courses, and other investments in employees’ professional development add to the cost of hiring an ML engineer.

Include these costs in your compensation budget before opening the role to avoid underestimating the total cost of a new hire.

Conclusion

Setting a realistic ML engineer salary budget is only part of the hiring challenge. You also need someone whose experience matches the models, data, and production systems they plan to build. That fit affects:

• The skills and experience you need.
• How long hiring may take.
• The compensation you need to offer.

Geomotiv helps companies hire ML engineers for projects across several industries:

• AdTech. Fraud and invalid traffic detection, bid optimization and shading, budget pacing, and prediction systems for better targeting.
• eCommerce. Product recommendations, demand forecasting, and personalized product rankings.
• Streaming. Video content recommendations, viewer preference modeling, and churn prediction.
• Healthcare. Medical imaging models, early disease detection, patient outcome prediction, and treatment response analysis.
• FinTech and banking. Transaction fraud detection, credit risk scoring, and loan default prediction systems.

Our HR team sources candidates from Geomotiv’s own network and the open market, identifying suitable candidates within 1-2 weeks. Our tech leads can interview shortlisted engineers to assess their ML expertise, production experience, and fit for your project’s technical requirements.

You can hire an individual ML engineer to join your existing team or build a dedicated team with the other engineering roles your project requires.