ML Engineer Visa Sponsorship Jobs in California
ML engineer visa sponsorship jobs in California sit at the center of the global AI industry, with major employers spanning San Francisco, the South Bay, and Los Angeles. Companies like Google, Meta, Apple, and a dense tier of AI-focused startups regularly sponsor H-1B and other work visas for qualified ML engineers across the state.
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Get Access To All JobsAbout us
We are Sila, a next-generation battery materials company. Our mission is to power the world’s transition to clean energy. To create this future, our team is building a better lithium-ion battery from the inside out today. We engineer and manufacture ground-breaking battery materials that significantly increase the energy density of batteries, while reducing their size and weight. The result? Smaller more powerful batteries that can unlock innovation in consumer devices and accelerate the mass adoption of electric cars to eliminate our dependence on fossil fuels. We're tackling one of the biggest challenges of our time every day, and together we're redefining what's possible. Are you ready to be a part of a team committed to changing the world?
Who You Are
You are excited to build the intelligence layer for manufacturing operations: systems that help the factory understand what is happening, predict what is likely to happen next, and respond earlier and better as a result. You are a strong technical builder who likes hard problems with real operational consequences. You can take an ambiguous manufacturing problem and turn it into a working system that engineers and operations teams actually use. You are comfortable working across software, data, engineering logic, and manufacturing systems. You know how to deal with noisy plant data, imperfect systems, and messy failure modes. You do not stop at visibility. You build systems that drive action.
We are looking for someone who has built and shipped systems that changed how an operation runs.
Build Manufacturing Intelligence Systems
- Build in-house production systems that ingest plant telemetry, live data feeds, event logs, quality data, maintenance history, and operational context to improve manufacturing prediction and closed-loop response
- Reconstruct equipment and process behavior from raw data and surface meaningful deviations between expected and actual execution
- Develop systems that identify process drift, classify fault patterns, and quantify operational risk before failures, downtime, or quality losses fully materialize
- Turn raw manufacturing signals into reliable services and applications that improve uptime, yield, and execution speed
Develop Models That Matter
- Build and deploy machine learning models for anomaly detection, fault classification, process monitoring, quality prediction, forecasting, and related manufacturing use cases
- Develop models that connect recipe conditions, process parameters, equipment behavior, and intermediate process results to downstream product quality and performance outcomes
- Build feedforward and feedback models that use upstream signals, in-process data, and downstream results to improve decisions during execution
- Apply AI models and agentic workflows only where they materially improve engineering execution, diagnosis, knowledge retrieval, or workflow automation
- Build hybrid solutions that combine deterministic engineering logic, statistical methods, optimization, machine learning, and foundation models where each adds the most value
- Convert model outputs into practical operational logic that supports triage, escalation, intervention, and action
Deploy Into Real Operations
- Design and deploy production-grade APIs, model services, pipelines, and internal tools that are reliable enough for day-to-day plant use
- Build workflows for feature generation, inference, event detection, and feedback into operational systems
- Partner closely with Manufacturing, Process Engineering, Controls, Quality, Data Systems, and Software teams to ensure outputs are technically sound and tied to real plant actions
- Help define the architecture and roadmap for operations intelligence across manufacturing and adjacent factory workflows
Qualifications
- Bachelor’s, Master’s, or PhD in Engineering, Computer Science, Operations Research, Industrial Engineering, or a related technical field
- Strong programming skills in Python and experience building production-quality software, internal applications, or data products beyond notebooks and dashboards
- Strong experience with scientific computing and machine learning libraries such as pandas, NumPy, SciPy, scikit-learn, statsmodels, PyTorch, TensorFlow, XGBoost, or equivalent tools
- Experience building and deploying software services, APIs, data pipelines, or internal platforms using tools such as FastAPI, Flask, SQL, Spark, Airflow, dbt, or similar technologies
- Experience working with time-series, sensor, event, equipment, MES, historian, quality, or other industrial data
- Experience training, validating, and deploying custom models for prediction, classification, anomaly detection, forecasting, optimization, or control-related use cases
- Strong systems thinking and the ability to translate ambiguous plant problems into robust technical solutions
- Experience taking technical systems from concept to deployment with measurable real-world impact
- Strong written and verbal communication skills and the ability to work effectively across technical and operational teams
Preferred Qualifications
- Experience building models that connect process conditions or recipe parameters to downstream quality or product performance outcomes
- Experience with predictive maintenance, process monitoring, fault analysis, quality prediction, or root-cause analysis in industrial settings
- Familiarity with MES, historians, plant systems architecture, or controls-adjacent environments
- Experience with sequence modeling, multivariate analysis, optimization, simulation, or hybrid physics and data-driven approaches
- Experience using modern AI workflows, including LLMs or agentic systems, in practical engineering or operational contexts
- Manufacturing experience is a plus, but we welcome candidates from adjacent operational domains with strong applied modeling and deployment experience
The starting base pay for this role is between $151,000 and $177,500 at the time of posting. The actual base pay depends on many factors, such as education, experience, and skills. Base pay is only one part of Sila’s competitive Total Rewards package that can include benefits, perks, equity. The base pay range is subject to change and may be modified in the future.
Working at Sila
We believe that building a diverse team at Sila helps us amplify our individual talents. We are an equal opportunity employer and committed to creating an inclusive environment where good ideas are free to come from anyone. We are proud to celebrate diversity and all qualified applicants are considered for employment without regard to gender, race, sexual orientation, religion, age, disability, national origin, or any other status protected by law.
About us
We are Sila, a next-generation battery materials company. Our mission is to power the world’s transition to clean energy. To create this future, our team is building a better lithium-ion battery from the inside out today. We engineer and manufacture ground-breaking battery materials that significantly increase the energy density of batteries, while reducing their size and weight. The result? Smaller more powerful batteries that can unlock innovation in consumer devices and accelerate the mass adoption of electric cars to eliminate our dependence on fossil fuels. We're tackling one of the biggest challenges of our time every day, and together we're redefining what's possible. Are you ready to be a part of a team committed to changing the world?
Who You Are
You are excited to build the intelligence layer for manufacturing operations: systems that help the factory understand what is happening, predict what is likely to happen next, and respond earlier and better as a result. You are a strong technical builder who likes hard problems with real operational consequences. You can take an ambiguous manufacturing problem and turn it into a working system that engineers and operations teams actually use. You are comfortable working across software, data, engineering logic, and manufacturing systems. You know how to deal with noisy plant data, imperfect systems, and messy failure modes. You do not stop at visibility. You build systems that drive action.
We are looking for someone who has built and shipped systems that changed how an operation runs.
Build Manufacturing Intelligence Systems
- Build in-house production systems that ingest plant telemetry, live data feeds, event logs, quality data, maintenance history, and operational context to improve manufacturing prediction and closed-loop response
- Reconstruct equipment and process behavior from raw data and surface meaningful deviations between expected and actual execution
- Develop systems that identify process drift, classify fault patterns, and quantify operational risk before failures, downtime, or quality losses fully materialize
- Turn raw manufacturing signals into reliable services and applications that improve uptime, yield, and execution speed
Develop Models That Matter
- Build and deploy machine learning models for anomaly detection, fault classification, process monitoring, quality prediction, forecasting, and related manufacturing use cases
- Develop models that connect recipe conditions, process parameters, equipment behavior, and intermediate process results to downstream product quality and performance outcomes
- Build feedforward and feedback models that use upstream signals, in-process data, and downstream results to improve decisions during execution
- Apply AI models and agentic workflows only where they materially improve engineering execution, diagnosis, knowledge retrieval, or workflow automation
- Build hybrid solutions that combine deterministic engineering logic, statistical methods, optimization, machine learning, and foundation models where each adds the most value
- Convert model outputs into practical operational logic that supports triage, escalation, intervention, and action
Deploy Into Real Operations
- Design and deploy production-grade APIs, model services, pipelines, and internal tools that are reliable enough for day-to-day plant use
- Build workflows for feature generation, inference, event detection, and feedback into operational systems
- Partner closely with Manufacturing, Process Engineering, Controls, Quality, Data Systems, and Software teams to ensure outputs are technically sound and tied to real plant actions
- Help define the architecture and roadmap for operations intelligence across manufacturing and adjacent factory workflows
Qualifications
- Bachelor’s, Master’s, or PhD in Engineering, Computer Science, Operations Research, Industrial Engineering, or a related technical field
- Strong programming skills in Python and experience building production-quality software, internal applications, or data products beyond notebooks and dashboards
- Strong experience with scientific computing and machine learning libraries such as pandas, NumPy, SciPy, scikit-learn, statsmodels, PyTorch, TensorFlow, XGBoost, or equivalent tools
- Experience building and deploying software services, APIs, data pipelines, or internal platforms using tools such as FastAPI, Flask, SQL, Spark, Airflow, dbt, or similar technologies
- Experience working with time-series, sensor, event, equipment, MES, historian, quality, or other industrial data
- Experience training, validating, and deploying custom models for prediction, classification, anomaly detection, forecasting, optimization, or control-related use cases
- Strong systems thinking and the ability to translate ambiguous plant problems into robust technical solutions
- Experience taking technical systems from concept to deployment with measurable real-world impact
- Strong written and verbal communication skills and the ability to work effectively across technical and operational teams
Preferred Qualifications
- Experience building models that connect process conditions or recipe parameters to downstream quality or product performance outcomes
- Experience with predictive maintenance, process monitoring, fault analysis, quality prediction, or root-cause analysis in industrial settings
- Familiarity with MES, historians, plant systems architecture, or controls-adjacent environments
- Experience with sequence modeling, multivariate analysis, optimization, simulation, or hybrid physics and data-driven approaches
- Experience using modern AI workflows, including LLMs or agentic systems, in practical engineering or operational contexts
- Manufacturing experience is a plus, but we welcome candidates from adjacent operational domains with strong applied modeling and deployment experience
The starting base pay for this role is between $151,000 and $177,500 at the time of posting. The actual base pay depends on many factors, such as education, experience, and skills. Base pay is only one part of Sila’s competitive Total Rewards package that can include benefits, perks, equity. The base pay range is subject to change and may be modified in the future.
Working at Sila
We believe that building a diverse team at Sila helps us amplify our individual talents. We are an equal opportunity employer and committed to creating an inclusive environment where good ideas are free to come from anyone. We are proud to celebrate diversity and all qualified applicants are considered for employment without regard to gender, race, sexual orientation, religion, age, disability, national origin, or any other status protected by law.
ML Engineer Job Roles in California
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Search ML Engineer Jobs in CaliforniaML Engineer Jobs in California: Frequently Asked Questions
Which companies in California sponsor visas for ML engineers?
Large technology companies with established immigration programs, including Google, Apple, Meta, Amazon Web Services, and Microsoft's Bay Area offices, consistently file H-1B petitions for ML engineers. Beyond the major players, California-based AI startups and research labs such as OpenAI, Anthropic, and Scale AI have also sponsored work visas for machine learning roles in recent years.
Which visa types are most common for ML engineer roles in California?
The H-1B is the most common visa for ML engineers in California because machine learning roles at the bachelor's level and above qualify as specialty occupations. Candidates already in the U.S. on F-1 OPT, including the 24-month STEM extension, frequently use that period to secure H-1B sponsorship. O-1A visas appear less often but are an option for engineers with a documented record of significant contributions or recognition in the field.
Which cities in California have the most ML engineer visa sponsorship jobs?
The San Francisco Bay Area, including San Francisco, Sunnyvale, Mountain View, and Menlo Park, accounts for the largest share of ML engineer sponsorship activity in the state. Los Angeles has grown steadily as a secondary hub, driven by companies in media technology, autonomous vehicles, and defense AI. San Diego has a smaller but active cluster tied to biotech AI and defense contractors.
How to find ml engineer visa sponsorship jobs in California?
Migrate Mate is built specifically for international candidates seeking visa sponsorship jobs, including ML engineer roles in California. You can filter by state and role to surface companies with active sponsorship history rather than sifting through listings that may not lead to a petition. Reviewing the Department of Labor's H-1B disclosure data alongside Migrate Mate listings helps confirm which California employers have filed for similar positions recently.
Are there any California-specific considerations for ML engineers seeking visa sponsorship?
California's prevailing wage requirements under H-1B rules often reflect some of the highest wage levels in the country given the state's cost of living, meaning employers must offer competitive compensation that meets Department of Labor standards for the relevant metropolitan area. The state's large research university pipeline, particularly UC Berkeley, Stanford, and UCLA, also creates significant competition for sponsorship slots, so candidates with specialized expertise in areas like large language models or computer vision tend to stand out.
What is the prevailing wage for sponsored ml engineer jobs in California?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.
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