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Principal Machine Learning Engineer Jobs in California

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Principal Machine Learning Engineer

San Francisco, CA · On-site +1

$159.10K - $213.20K/yr

Principal Software Engineer For Backend Development Our client is seeking a Principal Software ... Collaborating with AI teams to integrate advanced machine learning models into game development ...

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Principal Machine Learning Engineer information

See California salary details

$73K

$145.3K

$209.7K

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

As of May 28, 2026, the average yearly pay for principal machine learning engineer in California is $145,292.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,900.00 and $170,700.00 per year, depending on experience, location, and employer.

What does a Principal Machine Learning Engineer do?

A Principal Machine Learning Engineer leads the design, development, and deployment of machine learning models and systems. They set technical strategy, mentor engineers, and collaborate with cross-functional teams to solve complex AI challenges. Their role often includes researching new algorithms, optimizing model performance, and ensuring scalability in production environments. Additionally, they work closely with data scientists, software engineers, and product managers to align ML initiatives with business objectives.

What are the key skills and qualifications needed to thrive in the Principal Machine Learning Engineer position, and why are they important?

To thrive as a Principal Machine Learning Engineer, you need advanced expertise in machine learning algorithms, statistical analysis, software engineering, and a strong background in computer science or related fields, often supported by a master's or PhD degree. Familiarity with tools such as Python, TensorFlow, PyTorch, cloud platforms (AWS, GCP, Azure), and relevant certifications strengthens technical capability. Leadership, strategic thinking, effective communication, and mentorship are vital soft skills for guiding teams and collaborating across departments. These competencies are essential for driving innovation, ensuring technical excellence, and influencing organizational AI initiatives.

What types of projects and responsibilities can a Principal Machine Learning Engineer typically expect in this role?

Principal Machine Learning Engineers are often tasked with leading the design, development, and deployment of large-scale machine learning models and systems that address key business challenges. In this role, you will collaborate closely with data scientists, engineers, and product managers to define project requirements, architect solutions, and ensure high-quality delivery. You may also guide research initiatives, oversee code and model reviews, and mentor junior engineers, helping to shape the technical direction of the team. Typical responsibilities can range from prototyping and optimizing algorithms to ensuring models are scalable, reliable, and aligned with organizational goals.

Will MLE be replaced by AI?

Principal Machine Learning Engineers design, develop, and oversee AI and machine learning systems, and their roles involve understanding complex algorithms, data management, and model deployment. While AI automates certain tasks, MLE roles focus on building and maintaining AI infrastructure, which requires human expertise, critical thinking, and ongoing innovation that AI cannot fully replace. The role is expected to evolve alongside advancements in AI technology but remains essential for guiding AI development and ensuring ethical, effective implementation.
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Principal Machine Learning Engineer jobs near you
Infographic showing various Principal Machine Learning Engineer job openings in California as of May 2026, with employment types broken down into 95% Full Time, 3% Part Time, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $145,292 per year, or $69.9 per hour.

Principal Machine Learning Engineer

Edison Scientific Inc.

San Francisco, CA • On-site, Remote

$275K - $350K/yr

Other

Medical, Retirement

This job post has expired today. Applications are no longer accepted.

Job description

Principal Machine Learning Engineer

San Francisco, CA

About

Edison Scientific builds and commercializes AI agents for science. Scientific discovery moves too slowly, and autonomous AI agents are how we intend to fix that. We're assembling a team of top researchers and engineers across AI and biology to build an AI scientist.

Role

As a Principal Machine Learning Engineer at Edison Scientific, you play a central role in building the models and agents that accelerate scientific discovery. You will work on both cutting edge research and practical engineering, bridging advanced machine learning concepts with robust, reliable software that real scientists depend on.

This role is on-site at our San Francisco office in the Dogpatch neighborhood. Our office is a converted warehouse with high ceilings, open space, and a team excited about what we're building.

Responsibilities
  • Interpret qualitative challenges in building AI agents for science as well-formulated optimizable problems
  • Build appropriate environments in which to train and deploy AI agents that solve scientific tasks
  • Work with scientists to formulate training data pipelines, and scale them, ensuring observability and reproducibility
  • Lead training of large-scale LLM-based systems, including building internal infrastructure to improve the efficiency of experimentation and production training runs
  • Build efficient and flexible inference infrastructure, supporting complex sampling algorithms and custom architectures
  • Develop and extend our experimentation platform for internal tools and projects.
  • Collaborate closely with a multidisciplinary team of AI researchers, chemists, biologists, fostering an environment of innovation and discovery.
Qualifications
  • 8-10+ years of strong track record of work in applied ML research and application of ML methods to solving real-world problems
  • Experience working across the ML lifecycle: data pipelines and provenance, model training, model deployment, and validation in production systems.
  • Fluency in PyTorch, Jax or equivalent framework.
  • Demonstrated experience with experimentation in academic or industry settings.
  • Strong programming expertise with the capability to adapt to various technical challenges in the data, ML, and LLM software stack.
Bonus Points For
  • PhD in Machine Learning, Computer Science, or other quantitative field
  • Familiarity with leveraging and managing distributed computing resources
  • Background architecting complex distributed systems

Salary

$275,000 - $350,000 • Offers equity

Why join us?

  • Competitive salary and equity
  • Full healthcare coverage — we pay 100% of premiums for you and your dependents
  • Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
  • 401(k) company matching
  • $300 health and wellness benefit
  • Lunch is on us every day you're in the office, and dinner is on us when you're working late
  • Regular team offsites and company events
  • A fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it

Apply