Compensación
Datos de mercado- p25
- 128.704 US$
- median
- 197.500 US$
- p75
- 240.000 US$
Base anual, USD, según vacantes públicas · basado en n=311 · Actualizado noviembre de 2026. Cómo lo calculamos
Carrera y salario · engineering
Machine Learning Engineer
Datos de mercado para este puesto: salario, demanda y habilidades. Ver vacantes activas más abajo.
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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Qué hace un Machine Learning Engineer
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 $140,000 for a senior ML engineer will not work. Be realistic about what the market pays, especially if you're in a high-cost area or competing with FAANG companies.
The path forward
ML engineering is a stable, well-compensated career with clear growth. Senior ML engineers move into staff roles, technical leadership, or founding their own companies. The field is still young enough that there's room to specialize and become an expert in a domain or technique.
The 476 open roles suggest this isn't a temporary spike. Companies are building ML into their core products and operations. That means sustained demand, competitive pay, and interesting problems to solve.
Por seniority · mediana global
Intern37.036 US$
Junior60.183 US$+23.147 US$
Mid210.000 US$+149.817 US$
Senior137.280 US$
Staff157.401 US$+20.121 US$
Principal194.437 US$+37.036 US$
Director222.214 US$+27.777 US$
VP277.767 US$+55.553 US$
C-level370.356 US$+92.589 US$
Where would you land?
~50th percentile — about 197.500 US$ would out-earn an estimated 50% of Machine Learning Engineers.
Estimated from the p25–p75 band. For orientation, not an offer.
Vacantes abiertas
Puestos de Machine Learning Engineer en vivo
Las vacantes más recientes registradas en el mercado. Postúlate directamente, sin intermediarios.
- Edge AI Solution EngineerHPUShoy
- AI Security EngineerJanus Henderson InvestorsUKhoy
- Founding AI EngineerSaplingRemoteRemotohoy
- Applied AI Engineer - CanadaSaransh IncMont-Royal, Montréalhoy
- Applied AI Engineer - CanadaSaransh IncMont-Royal, Montréalhoy
- Field Application Engineer - EmulationSiemens AGOttawa, Ottawa regionhoy
- Machine Learning Engineer III, Routing CostMapboxUKayer
- PhD Machine Learning Engineer, InternStripeTorontoayer
- Master's Machine Learning Engineer, InternStripeTorontoayer
- Application EngineerBosch GroupPlymouth, MI, USayer
- Application Calibration EngineerBosch GroupPlymouth, MI, USayer
- Senior Staff Machine Learning Engineer, Marketing TechnologyAirbnbRemote (USA)Remotoayer
Vacantes en otras empresas
Vacantes activas de Machine Learning Engineer
Puestos de otras empresas que están contratando Machine Learning Engineer ahora mismo, tomados directamente de sus propios portales de empleo.
- 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
Trayectorias afines
Puestos relacionados
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 →
Dónde se paga mejor
Compensación por región
Base anual mediana en USD por región. Contratar globalmente amplía el rango del que puedes reclutar, y el arbitraje que puedes capturar.
Regiónp25Medianap75Fuente
US120.000 US$140.000 US$140.000 US$Datos de mercado
CA92.192 US$108.461 US$127.984 US$Estimado
GB87.695 US$103.171 US$121.741 US$Estimado
UY49.469 US$58.199 US$68.675 US$Estimado
MX47.220 US$55.553 US$65.553 US$Estimado
CR44.972 US$52.908 US$62.431 US$Estimado
CL44.972 US$52.908 US$62.431 US$Estimado
BR44.972 US$52.908 US$62.431 US$Estimado
CO38.226 US$44.972 US$53.067 US$Estimado
AR38.226 US$44.972 US$53.067 US$Estimado
PE35.977 US$42.326 US$49.945 US$Estimado
Bueno saberlo
Preguntas frecuentes
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.
Kit de contratación
Plantillas para contratar Machine Learning Engineer
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Las cifras de salario y demanda se derivan de vacantes públicas y de la línea base BLS OEWS. Lee cómo lo calculamos.