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Machine Learning Software Engineer Jobs in San Francisco, CA

You'll partner closely with software engineers, product managers, and data teams to build models ... Design, build, and deploy machine learning models into production * Develop scalable ML pipelines ...

Sr. Machine Learning Engineer

San Mateo, CA · On-site

$139K - $184K/yr

Veryfi is seeking a Sr. Machine Learning Engineer who will sit at the intersection of software engineering and data science. The role involves leveraging big data tools to redefine raw data into ...

... Learning Engineer to develop and deploy lightweight machine learning models for edge AI ... The role involves collaborating with hardware and software teams, optimizing models for embedded ...

Strong foundation in machine learning and software engineering * Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered

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 ...

Showing results 41-60

Machine Learning Software Engineer information

See San Francisco, CA salary details

$74.8K

$173.8K

$242.1K

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

As of Sep 2, 2026, the average yearly pay for machine learning software engineer in San Francisco, CA is $173,808.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,400.00 and $203,800.00 per year, depending on experience, location, and employer.

What does a machine learning software engineer do?

A Machine Learning Software Engineer designs, develops, and deploys machine learning models within software applications. They work on data preprocessing, model training, optimization, and integration into production systems. Their role requires expertise in programming (Python, Java, or C++), machine learning frameworks (TensorFlow, PyTorch, or Scikit-learn), and cloud platforms. They collaborate with data scientists and software engineers to build scalable ML solutions.

What are the key skills and qualifications needed to thrive as a machine learning software engineer?

To thrive as a Machine Learning Software Engineer, you need a solid understanding of programming (especially Python), algorithms, data structures, and mathematics, ideally backed by a degree in computer science, engineering, or a related field. Experience with frameworks such as TensorFlow or PyTorch, familiarity with cloud platforms (AWS, Azure, or GCP), and relevant certifications in data science or machine learning are highly valuable. Strong problem-solving skills, effective communication, and the ability to work collaboratively with cross-functional teams set outstanding candidates apart. These competencies are crucial for building deployable, scalable, and maintainable machine learning solutions that address real business challenges.

Is machine learning software engineer a high paying job?

Machine learning software engineers typically earn high salaries due to the specialized skills required, such as proficiency in programming languages like Python and experience with frameworks like TensorFlow. Salaries vary based on experience, location, and industry, but overall, it is considered a well-compensated role in the tech field.

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

The most popular types of Machine Learning Software Engineer jobs in San Francisco, CA are:

What cities near San Francisco, CA are hiring for Machine Learning Software Engineer jobs?

Cities near San Francisco, CA with the most Machine Learning Software Engineer job openings:

Infographic showing various Machine Learning Software Engineer job openings in San Francisco, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $173,808 per year, or $83.6 per hour.

Software Engineer, Machine Learning

Mercor

San Francisco, CA • On-site

$130K - $500K/yr

Full-time

Medical, Dental, Vision

Re-posted 3 days ago


Job description

About Mercor
Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You'll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
About the Role
As a Machine Learning Engineer on the Marketplace team, you will build the models and decision systems that power Mercor's hiring engine. This includes search and ranking, candidate-job matching, marketplace recommendations, personalization, and allocation decisions across a rapidly growing talent network.
This is an applied ML role with direct product and revenue impact. You will work on problems shaped by real marketplace constraints: sparse and delayed labels, cold start, noisy feedback, heterogeneous supply and demand, and the need to optimize across speed, quality, and conversion simultaneously.
What You'll Build
  • Ranking and matching systems that determine which candidates and opportunities are surfaced
  • Models for recommendation, personalization, and marketplace optimization
  • Retrieval, scoring, and decision pipelines operating at global scale
  • Feedback loops that learn from downstream hiring outcomes, not just top-of-funnel engagement
  • Real-time and batch inference systems embedded in product-critical workflows

Example Problems
  • Improve candidate-job matching using embeddings, structured attributes, and behavioral signals
  • Optimize ranking toward long-term hiring outcomes under delayed and incomplete labels
  • Design models that balance marketplace objectives such as fill rate, quality, speed, and conversion
  • Build systems for candidate allocation, opportunity routing, and liquidity optimization
  • Develop evaluation and experimentation frameworks that connect model performance to business results

What We're Looking For
  • Strong track record of shipping ML systems into production
  • Experience with ranking, recommendation, search, matching, or marketplace problems
  • Good judgment on model design, objective functions, evaluation, and tradeoffs
  • Comfort working across the full applied ML stack: data, features, training, inference, and iteration
  • Strong engineering fundamentals and a bias toward simple, robust systems

Why This Role
This role sits on a core decision layer of the product. Your work will directly shape how talent is discovered, matched, and hired, and will influence fundamental marketplace outcomes across quality, speed, and revenue.
Tech Stack
Python, Go, embeddings, fine-tuning, RAG, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform
Benefits
  • Bi-annual performance bonus structure
  • Generous equity grant vested over 4 years
  • Up to $15k Relocation bonus
  • $10K housing bonus (if you live within 0.5 miles of our office)
  • $1.5K monthly stipend for meals
  • Free Equinox membership
  • $200 monthly laundry reimbursement
  • $200 monthly personal wellness reimbursement
  • Health, Dental, Vision insurance