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Machine Learning Software Engineer Jobs in Fremont, CA

Ema

Software Engineer, Machine Learning

San Francisco, CA · On-site

$135K - $200K/yr

Who You Are We're looking for innovative and passionate Machine Learning Engineers to join our team ... Good understanding of software development principles, data structures, and algorithms. * Excellent ...

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All JobsMachine Learning Software Engineer JobsMachine Learning Software Engineer Jobs in Fremont, CA

Machine Learning Software Engineer information

See Fremont, CA salary details

$69.5K

$161.5K

$225K

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

As of Jul 13, 2026, the average yearly pay for machine learning software engineer in Fremont, CA is $161,489.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,400.00 and $189,400.00 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 in the Machine Learning Software Engineer position, and why are they important?

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.

What are the day-to-day responsibilities of a Machine Learning Software Engineer?

As a Machine Learning Software Engineer, your daily tasks typically include developing and optimizing machine learning models, collaborating with data scientists and product teams to define requirements, and integrating models into production systems. You’ll work extensively with large datasets to preprocess, analyze, and validate data, as well as monitor model performance and iterate on solutions when needed. It's common to participate in code reviews, contribute to architectural decisions, and maintain documentation for reproducibility and knowledge sharing. This role offers a dynamic and intellectually stimulating environment, making it ideal for those who enjoy solving complex technical problems and working at the intersection of engineering and data science.

What are popular job titles related to Machine Learning Software Engineer jobs in Fremont, CA? For Machine Learning Software Engineer jobs in Fremont, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Software Engineer jobs in Fremont, CA look for? The top searched job categories for Machine Learning Software Engineer jobs in Fremont, CA are:
What cities near Fremont, CA are hiring for Machine Learning Software Engineer jobs? Cities near Fremont, CA with the most Machine Learning Software Engineer job openings:
Machine Learning Software Engineer jobs near you
Infographic showing various Machine Learning Software Engineer job openings in Fremont, CA as of July 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $161,489 per year, or $77.6 per hour.

Software Engineer, Machine Learning Platform

Chime Financial, Inc

San Francisco, CA • On-site

Other

Re-posted 28 days ago

Job description

About the role 

Chime's Machine Learning Platform (MLP) team builds and operates the infrastructure, tooling, and developer experience that powers machine learning across the company. We enable data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently.

As a Machine Learning Platform Engineer, you will design and build scalable systems that support model training, feature computation, real-time inference, and experimentation. You'll work at the intersection of distributed systems, cloud infrastructure, and applied machine learning.

This role focuses on building robust foundations that allow ML teams to move quickly while maintaining reliability, governance, and cost efficiency.

The base salary offered for this role and level of experience will begin at $187,000.00 and goes up to $259,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

In this role, you can expect to
  • Design, build, and operate scalable ML infrastructure on AWS
  • Develop distributed training and batch processing systems using Ray
  • Build and maintain infrastructure-as-code using Terraform
  • Support and evolve the feature store and feature pipelines
  • Develop data ingestion and streaming systems (e.g., Kinesis, Kafka, Flink, Spark, or similar technologies)
  • Improve CI/CD workflows for ML models and platform components
  • Enhance observability, reliability, and cost visibility across ML workloads
  • Partner closely with Data Science and ML Engineering teams to improve developer experience
  • Contribute to platform architecture decisions and technical roadmaps
  • Participate in on-call rotations to support production systems
To thrive in this role, you have
  • 5+ years of experience in ML infrastructure, platform engineering, or production ML systems
  • Knowledge of the machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment
  • Experience with distributed systems, cloud computing, or large-scale data processing
  • Strong foundation in computer science and software engineering principles
  • Deeply interested in the impact and evolution of advanced AI technologies
  • Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code
  • Experience with containerization technologies such as Docker and Kubernetes, and orchestration systems
  • Knowledge of cloud platforms such as AWS and distributed computing frameworks such as Spark and Ray
  • Experience with GPU programming(CUDA) and GPU costs/optimization
  • Strong programming skills in Python, Go, Scala, Java or similar languages
  • Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)
  • Solid understanding of software engineering fundamentals (testing, version control, code review, observability)
Nice-to-have
  • Experience with distributed compute frameworks such as Ray
  • Experience building or operating a feature store
  • Experience with real-time ML systems or model serving
  • Familiarity with streaming technologies (Kafka, Kinesis, Flink, Spark Streaming, etc.)
  • Experience supporting ML lifecycle workflows (training, evaluation, deployment, monitoring)
  • Knowledge of ML experimentation platforms and model governance practices

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