Compensation
Market data- p25
- $128,704
- median
- $197,500
- p75
- $240,000
Annual base, USD, from public postings · based on n=311 · Updated November 2026. How we calculate this
Career & pay · engineering
Machine Learning Engineer
Market data for this role — salary, demand, and skills. See live openings below.
Machine learning engineers are in strong demand across 172 companies with 476 active openings, commanding a median salary of $210,000 and a competitive range from $150,000 to $239,000.
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About the role
What a Machine Learning Engineer does
What machine learning engineers actually do
Machine learning engineers build, train, and deploy models that learn from data to solve real business problems. Unlike data scientists who often focus on research and experimentation, ML engineers own the full lifecycle: they take experimental models into production, optimize them for speed and scale, and maintain them as performance degrades over time.
Day-to-day work varies by company and seniority, but typically includes writing training pipelines, debugging model performance in production, collaborating with data engineers on data infrastructure, and working with product teams to translate business requirements into model objectives. You might spend a morning tuning hyperparameters, an afternoon investigating why a model's accuracy dropped in production, and a few hours designing a feature store with your data engineering team.
The role sits at the intersection of software engineering and data science. You need solid coding skills—most work happens in Python, but you'll also write deployment code, APIs, and infrastructure-as-code. You also need to understand the math and statistics behind the models you're shipping, not just how to call a library.
The market right now
With 476 active postings across 172 companies, machine learning engineering remains one of the most in-demand technical roles. The breadth of hiring—from early-stage startups to Fortune 500 companies—reflects how broadly ML has become embedded in product strategy.
Compensation reflects this demand. The median salary is $210,000 annually. The 25th percentile sits at $150,000, typically for junior engineers or roles in lower cost-of-living markets. The 75th percentile reaches $239,000, common for senior engineers, those in high-cost metros, or roles requiring specialized expertise (computer vision, NLP, reinforcement learning). Total comp often includes equity and bonuses that can push effective earnings 20–40% higher.
Geography matters. San Francisco, New York, Seattle, and Boston command premiums. Remote-first companies often pay closer to Bay Area rates regardless of location. Smaller markets and non-tech hubs typically pay 15–25% less.
Skills that actually matter
**Core technical skills:** - Python (nearly universal; some roles also require Scala, Go, or C++) - ML frameworks: PyTorch, TensorFlow, scikit-learn, XGBoost - SQL and data querying - Version control (Git) and CI/CD pipelines - Cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
**Softer but critical skills:** - Debugging and troubleshooting—production models fail in unexpected ways - Communication—translating between data science and engineering teams - Systems thinking—understanding latency, throughput, and cost tradeoffs - Pragmatism—knowing when a simple heuristic beats a complex model
The gap between "can train a model" and "can ship a model" is where ML engineers add value. You need to care about inference latency, monitoring, retraining cadence, and data drift. You need to write code that other engineers can maintain.
How companies are hiring
Most ML engineering roles fall into a few patterns:
**Infrastructure and platform roles** focus on building the systems other ML engineers use: feature stores, training pipelines, model serving infrastructure. These roles skew toward software engineering skills and are common at large companies and well-funded startups.
**Product ML roles** embed you in a product team, owning models that directly impact users. You might build recommendation systems, fraud detection, or ranking models. These roles require balancing model performance with business metrics and shipping velocity.
**Research-adjacent roles** at AI labs, large tech companies, and specialized startups focus on novel architectures or pushing state-of-the-art. These typically require a strong publication record or PhD, and pay at the high end of the range.
**Domain-specific roles** in healthcare, finance, autonomous vehicles, or other specialized fields often pay premiums and require domain knowledge alongside ML expertise.
Hiring processes typically include a coding interview (usually Python), a machine learning design question ("How would you build a recommendation system?"), and sometimes a take-home project. Strong candidates often have a portfolio: GitHub repos, Kaggle competitions, or published work.
Why demand is strong and staying strong
ML engineering demand isn't a bubble. Companies have moved past "let's hire a data scientist" and now ask "how do we operationalize ML across the business?" That requires engineers who can scale models, integrate them into systems, and keep them running.
The gap between available talent and open roles remains wide. Many people can write a model in a notebook; far fewer can own a model in production. This is why the role commands strong compensation and why hiring managers often struggle to fill positions.
The field is also maturing. Early-stage companies that hired data scientists to do everything are now splitting roles: data scientists focus on research, ML engineers focus on production. This specialization is creating more roles and clearer career paths.
Getting hired
If you're early in your career, focus on demonstrating production-mindedness. Build projects that go beyond notebooks: deploy a model as an API, set up monitoring, write tests. Contribute to open-source ML projects. Kaggle competitions are fine but matter less than showing you can ship.
If you're transitioning from data science, emphasize your software engineering skills and any production experience. If you're coming from backend engineering, highlight any data or ML work and your ability to learn the domain quickly.
Interviewing well means: - Thinking out loud about tradeoffs (accuracy vs. latency, complexity vs. maintainability) - Asking clarifying questions before diving into solutions - Being honest about what you don't know - Showing familiarity with the company's actual ML challenges (read their blog, papers, or job description carefully)
Hiring well
For companies: the best ML engineers often aren't the ones with the most papers or Kaggle medals. Look for people who ask good questions, care about code quality, and have shipped something. A mid-level engineer who's owned a model in production is often more valuable than a junior researcher.
Structure interviews to assess both depth (can they debug a failing model?) and breadth (do they understand the systems around ML?). Ask about their worst production incident and what they learned. That answer tells you a lot.
Compensation matters. At $210,000 median, you're competing with other tech roles. Offering $140,000 for a senior ML engineer will not work. Be realistic about what the market pays, especially if you're in a high-cost area or competing with FAANG companies.
The path forward
ML engineering is a stable, well-compensated career with clear growth. Senior ML engineers move into staff roles, technical leadership, or founding their own companies. The field is still young enough that there's room to specialize and become an expert in a domain or technique.
The 476 open roles suggest this isn't a temporary spike. Companies are building ML into their core products and operations. That means sustained demand, competitive pay, and interesting problems to solve.
By seniority · global median
Intern$37,036
Junior$60,183+$23,147
Mid$210,000+$149,817
Senior$137,280
Staff$157,401+$20,121
Principal$194,437+$37,036
Director$222,214+$27,777
VP$277,767+$55,553
C-level$370,356+$92,589
Where would you land?
~50th percentile — about $197,500 would out-earn an estimated 50% of Machine Learning Engineers.
Estimated from the p25–p75 band. For orientation, not an offer.
Open positions
Live Machine Learning Engineer roles
The most recent openings tracked across the market. Apply directly — no middle layer.
- Edge AI Solution EngineerHPUStoday
- AI Security EngineerJanus Henderson InvestorsUKtoday
- Founding AI EngineerSaplingRemoteRemotetoday
- Applied AI Engineer - CanadaSaransh IncMont-Royal, Montréaltoday
- Applied AI Engineer - CanadaSaransh IncMont-Royal, Montréaltoday
- Field Application Engineer - EmulationSiemens AGOttawa, Ottawa regiontoday
- Machine Learning Engineer III, Routing CostMapboxUKyesterday
- PhD Machine Learning Engineer, InternStripeTorontoyesterday
- Master's Machine Learning Engineer, InternStripeTorontoyesterday
- Application EngineerBosch GroupPlymouth, MI, USyesterday
- Application Calibration EngineerBosch GroupPlymouth, MI, USyesterday
- Senior Staff Machine Learning Engineer, Marketing TechnologyAirbnbRemote (USA)Remoteyesterday
Openings elsewhere
Live Machine Learning Engineer openings
Roles from other companies hiring Machine Learning Engineers right now, sourced straight from their own job boards.
- PhD Machine Learning Engineer, InternStripeToronto
- Master's Machine Learning Engineer, InternStripeToronto
- Application EngineerBosch GroupPlymouth, MI, US
- Application Calibration EngineerBosch GroupPlymouth, MI, US
- Senior Staff Machine Learning Engineer, Marketing TechnologyAirbnbRemote (USA)
- PhD Machine Learning Engineer, InternStripeSan Francisco, New York City, Seattle
- Master's Machine Learning Engineer, InternStripeSan Francisco, New York City, Seattle
- Enterprise Application AI EngineergustoSan Francisco, CA - Hybrid
- Senior Machine Learning Engineer, Cosmos EngineeringNVIDIAUS, CA, Santa Clara
- Senior Machine Learning Engineer, Prediction - Autonomous DrivingNVIDIA2 Locations
Adjacent paths
Related roles
Software Engineer2,536 openView market data →Senior Software Engineer2,086 openView market data →Staff Software Engineer751 openView market data →DevOps Engineer575 openView market data →Engineering Manager506 openView market data →Fullstack Engineer438 openView market data →Principal Software Engineer331 openView market data →Data Engineer269 openView market data →
Where it pays
Compensation by region
Median annual base in USD by region. Hiring globally widens the band you can recruit from — and the arbitrage you can capture.
Regionp25Medianp75Source
US$120,000$140,000$140,000Market data
CA$92,192$108,461$127,984Estimated
GB$87,695$103,171$121,741Estimated
UY$49,469$58,199$68,675Estimated
MX$47,220$55,553$65,553Estimated
CR$44,972$52,908$62,431Estimated
CL$44,972$52,908$62,431Estimated
BR$44,972$52,908$62,431Estimated
CO$38,226$44,972$53,067Estimated
AR$38,226$44,972$53,067Estimated
PE$35,977$42,326$49,945Estimated
Good to know
Frequently asked
What's the salary range for a Machine Learning Engineer?
The median Machine Learning Engineer salary is $197,500. Most Machine Learning Engineer roles pay between $128,704 and $240,000 (USD, annual). Based on 311 salary observations.
Which companies hire Machine Learning Engineers?
Companies actively hiring Machine Learning Engineers include OpenAI, Reddit, ServiceNow, anthropic, Improving, Airbnb.
How do I get hired as a Machine Learning Engineer on diiirect?
Apply directly to 647 open Machine Learning Engineer postings on diiirect — no recruiters, no middle layer. Companies review your profile and hire you directly.
Hiring kit
Templates for hiring Machine Learning Engineers
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Salary and demand figures are derived from public job postings and the BLS OEWS baseline. Read how we calculate this.