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Lead Machine Learning Engineer Jobs in California

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only h1 candidate About the Role: Our direct client is hiring a Machine Learning Engineer for their ...

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

The Opportunity Adobe is looking for a Machine Learning Engineer who will apply AI and machine learning techniques to big-data problems to help Adobe better understand, lead and optimize the ...

Machine Learning Engineer Location: Fremont, CA once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a ...

The Opportunity Adobe is looking for a Machine Learning Engineer who will apply AI and machine learning techniques to big-data problems to help Adobe better understand, lead and optimize the ...

As a Machine Learning Engineer, you will design and build cutting-edge AI/ML systems that drive meaningful business outcomes at scale. You will work cross-functionally to bring innovative machine ...

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

We're looking for an exceptional Machine Learning Engineer to help build the systems that make this possible. In this role, you'll develop models, signals and evaluation frameworks that power ...

We lead the full product development cycle, integrating mechanical, electrical, thermal, and ... Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ...

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling advanced software systems to automate Design for Manufacturing analysis. Responsibilities : • ...

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

As a Senior Machine Learning Engineer, you will design, build, and scale advanced software systems that automate Design for Manufacturing analysis, leveraging deep learning and computer vision ...

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple ...

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Showing results 1-20

Lead Machine Learning Engineer information

See California salary details

$41.9K

$122.2K

$178.1K

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

As of Jul 13, 2026, the average yearly pay for lead machine learning engineer in California is $122,163.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,200.00 and $133,200.00 per year, depending on experience, location, and employer.

How much does a lead machine learning engineer make?

A lead machine learning engineer typically earns between $120,000 and $180,000 annually, depending on experience, location, and industry. Senior roles often include responsibilities such as designing models, leading teams, and working with advanced tools like TensorFlow or PyTorch.

How does a Lead Machine Learning Engineer typically collaborate with cross-functional teams during a project?

As a Lead Machine Learning Engineer, you'll frequently work alongside data scientists, software engineers, product managers, and sometimes domain experts to drive projects from conception to deployment. You are often responsible for translating business requirements into scalable machine learning solutions, coordinating model development, and ensuring integration with existing systems. Clear communication and the ability to explain complex technical concepts to non-technical stakeholders are essential, as you may need to guide team members and align everyone's efforts toward project goals. This collaborative environment fosters both technical and leadership growth.

What are the key skills and qualifications needed to thrive as a Lead Machine Learning Engineer, and why are they important?

To thrive as a Lead Machine Learning Engineer, you need advanced expertise in machine learning algorithms, data modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, cloud computing platforms, and version control systems is essential, along with relevant certifications. Strong leadership, collaboration, and problem-solving skills help you manage teams and communicate complex technical ideas effectively. These skills and qualities are crucial for driving successful AI initiatives, ensuring project delivery, and fostering innovation within cross-functional teams.

Will MLE be replaced by AI?

Lead Machine Learning Engineers (MLEs) design, develop, and oversee AI systems, and while AI automation can handle certain tasks, MLEs are essential for creating, tuning, and maintaining complex models. AI tools support MLEs but do not replace the need for human expertise in understanding data, ethical considerations, and system deployment. The role evolves with advancements in AI, emphasizing skills in model development, programming, and problem-solving.

What does a Lead Machine Learning Engineer do?

A Lead Machine Learning Engineer oversees the design, development, and deployment of machine learning models within an organization. They guide a team of engineers and data scientists, ensuring best practices in model architecture, data management, and production pipelines. Their responsibilities often include collaborating with stakeholders, mentoring junior team members, and staying up-to-date with the latest advancements in machine learning. Lead ML Engineers also play a key role in translating business objectives into technical solutions and ensuring scalability and reliability of AI systems.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position such as Lead Machine Learning Engineer or senior AI executive roles that offer compensation in this range, including salary, bonuses, and stock options. These roles usually require extensive experience, advanced skills in machine learning, deep learning, and data science, and often involve leadership responsibilities in developing AI strategies and solutions.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or at large tech companies can earn salaries approaching or exceeding $500,000 annually, especially when including bonuses and stock options. Achieving this level typically requires a strong educational background, specialized certifications, and a track record of impactful projects.
Infographic showing various Lead Machine Learning Engineer job openings in California as of July 2026, with employment types broken down into 92% Full Time, 4% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $122,163 per year, or $58.7 per hour.
Machine Learning Engineer

Machine Learning Engineer

Winaxis

Fremont, CA • On-site

Contractor

Re-posted 26 days ago


Job description

Title: Machine Learning Engineer

Location: Fremont, CA (Local) Onsite interview

Duration: 12+ Mos  

H1B

Only h1 candidate

About the Role:

Our direct client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting factory and warehouse operations. You will transform ambiguous problem statements into robust end-to-end solutions using a variety of machine learning techniques and tools, including supervised learning, convolutional neural networks, and modern frameworks such as PyTorch and Pandas.

You will collaborate closely with partners in production, process, controls, and quality to deliver solutions for the most challenging problems in our operations. Your work will involve evaluating and deploying models in production environments, ensuring rapid and reliable alerting systems, and addressing operational issues as they arise. You must be adept at handling diverse, heterogeneous datasets that span multiple modalities, including images, multi-spectral sensor outputs, voice, text, and tabular data.

Responsibilities

Design, develop, and deploy machine learning models for factory and warehouse environments.

Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges.

Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to model deployment and monitoring.

Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.

Ensure robust alerting and monitoring systems are in place for deployed models to address issues rapidly.

Work with diverse datasets, integrating multiple data types such as images, sensor data, voice, text, and tabular information.

Write clean, modular, and sustainable code to translate research ideas into production-ready solutions.

Minimum Requirements

In-depth knowledge of Python for high-performance, data-intensive applications.

Proficiency with at least one modern deep learning framework (e.g., PyTorch, Jax, TensorFlow).

Expertise in one or more of the following areas: computer vision, large language models, recommender systems, or operations research.

Foundational knowledge of statistics for model comparison and performance assessment.

Real-world experience deploying and maintaining machine learning solutions in production environments.

Passion for clean, sustainable, and modular code to bring research concepts to practical implementation.

Preferred Qualifications

Experience working in manufacturing, industrial automation, or warehouse environments.

Familiarity with multi-modal data integration and analysis.

Strong problem-solving skills and the ability to thrive in ambiguous, fast-paced settings.

Excellent communication skills for cross-functional teamwork.