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Senior Machine Learning Engineer Jobs in Davis, CA

Software Engineer

Sacramento, CA · On-site

$100 - $130/hr

Engineering - Sacramento, California, United States - Full Stack and AI About the team At OceanAI ... Hands‑on experience with AI and machine learning frameworks (TensorFlow, PyTorch) * Knowledge of ...

New

We are hiring an AI Engineer to embed with our mechanical and controls engineering teams and implement physical AI: systems where computer vision and machine learning models perceive the physical ...

New

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Senior Systems Engineer

Sacramento, CA · On-site

$113K - $155K/yr

... learning. We offer quality career resources, training, certifications, development opportunities ... UnitedHealthcare creates and publishes the Transparency in Coverage Machine-Readable Files on ...

New

Showing results 41-60

Senior Machine Learning Engineer information

See Davis, CA salary details

$64.3K

$136.8K

$198.3K

How much do senior machine learning engineer jobs pay per year?

As of Sep 2, 2026, the average yearly pay for senior machine learning engineer in Davis, CA is $136,792.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $155,100.00 per year, depending on experience, location, and employer.

What does a senior machine learning engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What are the key skills and qualifications needed to thrive as a senior machine learning engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

What is the difference between Senior Machine Learning Engineer vs Data Scientist?

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Davis, CA?

The most popular types of Machine Learning Engineer jobs in Davis, CA are:

What are popular job titles related to Senior Machine Learning Engineer jobs in Davis, CA?

For Senior Machine Learning Engineer jobs in Davis, CA, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning Engineer jobs in Davis, CA look for?

The top searched job categories for Senior Machine Learning Engineer jobs in Davis, CA are:

What cities near Davis, CA are hiring for Senior Machine Learning Engineer jobs?

Cities near Davis, CA with the most Senior Machine Learning Engineer job openings:

Infographic showing various Senior Machine Learning Engineer job openings in Davis, CA as of August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 28% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $136,792 per year, or $65.8 per hour.

Robotics Software Engineer

Terabase Energy

Woodland, CA

Full-time

Retirement

Posted 2 days ago

New


Job description

About Terabase Energy

Terabase Energy builds the automation and robotics systems that are transforming how utility-scale solar power plants are built. Our platforms combine robotics, controls, and software to increase construction speed, quality, and safety across the solar EPC lifecycle. As physical automation becomes central to how we deliver projects, we are building out a Physical AI capability that pairs machine perception and decision-making with the mechanical and controls systems already deployed in the field.

Job Description

We are hiring an AI Engineer to embed with our mechanical and controls engineering teams and implement physical AI: systems where computer vision and machine learning models perceive the physical world and drive real automation and mechatronic action. This is a hands-on, cross-disciplinary role focused on two outcomes - using AI vision to improve manufacturing and construction process performance, and using AI vision for automated material and component quality control (QC). This position reports directly to our Director of Factory Automation and will work very closely with the controls and mechanical engineering teams.

You will work at the intersection of ML models, sensors, and actuated hardware - not in a research silo. Success means AI systems that reliably run on real equipment, in real production and field environments, and materially move throughput, yield, and quality metrics.

Our systems run on production floors and active solar construction sites, not in a lab. A critical requirement of this role is building AI systems that are supportable and robust to real-world deployment conditions - dust, heat, cold, vibration, moisture, and intermittent connectivity - and that a field team can operate, monitor, and troubleshoot without a machine-learning background.

We're looking for an engineer with a proven track record shipping real-time automated systems into production - someone who has owned a system end-to-end across controls, perception, and the software layer that ties them together, not just a component of one.

Successful candidates will exhibit strong resourcefulness, analytical thinking/problem solving, excellent communication skills, and adaptability all coupled together by a superlative work ethic. Most importantly, you, like us, will be dedicated to accelerating the decarbonization of the global economy using digital and automation technology to further reduce the cost of utility-scale solar.

Responsibilities

        Partner closely with the Controls and Mechanical Engineering teams on automated work cells, PLC-controlled systems, and end-effector tooling - owning the full loop from real-time control and perception through the operator-facing software that runs on the equipment

        Engineer AI/vision systems to be robust and supportable in outdoor, active solar-construction environments - accounting for dust, temperature extremes, vibration, moisture, and intermittent connectivity - and maintainable by field personnel without ML expertise

        Design, train, and deploy computer vision models (object detection, defect/anomaly detection, segmentation) for real-time material and component QC on automated assembly and production lines

        Build perception pipelines that fuse camera, LiDAR, and other sensor data, and deploy low-latency inference at the edge (e.g., NVIDIA Jetson or similar) to support closed-loop equipment control and real-time vision-guided actions

        Develop and validate defect-classification and QC models against production tolerances - including comprehensive model and system testing - working with manufacturing and quality engineering to define acceptance criteria

        Collect, label, and manage training data from factory and field environments; build the data pipelines and tooling needed for continuous model improvement

        Instrument automated cells and production lines with the telemetry needed to evaluate AI system performance, uptime, and impact on cycle time, scrap rate, and yield

        Build lightweight operator-facing tools - dashboards, web UI - so field teams can monitor and interact with AI-enabled systems without ML expertise

        Support integration, commissioning, and on-site troubleshooting of AI-enabled automation equipment in test and production environments, working closely with controls engineers, and document system performance and failure modes for cross-functional stakeholders

        Support risk assessment and EHS review of newly designed AI-enabled machines, ensuring compliance with safety protocols

Requirements

Minimum Qualifications

        Bachelor's degree in Computer Science, Electrical Engineering, Mechanical Engineering, Robotics, or a related field

        5+ years of hands-on experience shipping automated, mechatronic, or robotic systems into production - spanning real-time control, perception, and the software that connects them

        Proficiency in Python and computer vision tooling (OpenCV or equivalent); experience with deep-learning frameworks (PyTorch, TensorFlow) is a plus but not required

        Working understanding of how AI perception connects to physical systems: sensors, actuators, motion control, PLCs, or robotic arms and end effectors

        Experience with robotic middleware (ROS/ROS2) and/or industrial communication protocols (EtherCAT, EtherNet/IP, CAN, Modbus)

        Demonstrated experience designing systems for reliability and supportability in harsh, uncontrolled, or outdoor environments (construction, industrial field equipment, automotive, aerospace, or similar) - not just lab or data-center conditions

        Willingness and ability to travel up to 30%

        Prefer experience in industrial manufacturing and/or construction industry

        Self-starter, able to thrive in a fast-paced and continually changing environment.

        Strong communication, customer relationship skills, and ability to communicate effectively and interact within a team environment.

        Proven skill in MS Suite of software (Outlook, Excel, PowerPoint, etc.)

Preferred Qualifications

        Experience with industrial machine vision systems (e.g., Cognex, Keyence) or camera/lighting setup for QC applications

        Experience with edge AI deployment tooling (TensorRT, Jetson, or similar embedded inference platforms)

        Use of robotic simulation software (e.g., KukaSim, Roboguide, Visual Components, NVIDIA Isaac Sim, Gazebo) for sim-to-real workflows and offline testing

        Exposure to reinforcement learning, imitation learning, or vision-language-action (VLA) / generalist robot policy models

        Experience taking an AI-enabled product from concept to shipped, including the software layer around it (web UI, APIs, data pipelines)

Benefits

Compensation And Benefits


Our salary ranges are determined by role, level, and location. This role offers a base salary of $130,000 - $160,000. Within each posted range, individual pay is determined (and may be greater or higher), dependent on work location and additional factors, including job-related skills, experience, and relevant education or training. Terabase offers competitive compensation along with a comprehensive benefits package, including:

Generous time off and holiday policy

Flexible time off

Comprehensive benefits package

Career progression

401k match

Stock options

Home office set up allowance

And much more!

Travel: Significant travel to customer and project sites.

Terabase is an equal opportunity employer. We recruit, hire, employ, train, promote, and compensate individuals based on job-related qualifications and abilities. We strongly encourage people of all backgrounds to apply.

We do not discriminate for any reason including race, color, sex, gender, age, religion or religious creed, national origin, ancestry, citizenship, marital status, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, military/ veteran status, or any other characteristic protected by law.

We offer a welcoming and inclusive environment in service to one another, our products, the diverse consumers we represent, and the communities we call home.

Principles only. This role is not open to receiving agency candidates, and any contingent submissions will not be considered. Terabase Energy does not utilize third-party recruitment agencies. Please contact our Recruiting team at careers@terabase.energy with any staffing-related inquiries.