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Software Engineer Mle Jobs in California (NOW HIRING)

Over the last decade, software has transformed the digital world. But the physical world, where ... What we're looking for * 10+ years of non-internship professional MLE experience. * Deep expertise ...

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Over the last decade, software has transformed the digital world. But the physical world, where ... What we're looking for * 4+ years of non-internship professional MLE experience. * Deep expertise ...

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... engineering with a focus on model optimization, distillation, and deployment. • Hands-on ...

Required : • 12+ years of software engineering experience delivering production web systems. • ... Preferred : • Hands-on ML platform depth (MLE experience or strong familiarity with DL frameworks ...

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

Required : • 4+ years of non-internship professional MLE experience. • Deep expertise in ... engineering with a focus on model optimization, distillation, and deployment. • Hands-on ...

About the Role As a Machine Learning Engineer in OpenAI's Integrity team, you will work with some ... Work closely with researchers, software engineers, and product managers to understand complex ...

New

Senior AI/ML Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Minimum of 5+ years of software engineering experience, with significant recent focus on AI/ML ... MLE experience: hands-on experience building, training, and serving at least one custom model in ...

About the Role As a Machine Learning Engineer in OpenAI's Integrity team, you will have the ... Work closely with researchers, software engineers, and product managers to understand complex ...

Showing results 41-60

Software Engineer Mle information

What is the difference between Software Engineer Mle vs Data Scientist?

AspectSoftware Engineer MleData Scientist
Required CredentialsBachelor's in CS or related, knowledge of ML frameworksBachelor's or higher in CS, statistics, or related
Work EnvironmentDevelops ML models, integrates into software productsAnalyzes data, builds predictive models, reports insights
Employer & Industry UsageTech companies, startups, AI-focused firmsTech, finance, healthcare, research institutions

While both roles involve machine learning, Software Engineer Mle focuses on integrating ML models into software applications, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and work environment.

What cities in California are hiring for Software Engineer Mle jobs?

Cities in California with the most Software Engineer Mle job openings:

Infographic showing various Software Engineer Mle job openings in California as of August 2026, with employment types broken down into 1% As Needed, 90% Full Time, 6% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.

Staff Machine Learning Engineer

Atoms

San Francisco, CA

Full-time

Medical, Dental, Vision, Life, Retirement

Re-posted 24 days ago


Job description

Who we are 

Atoms is building the machines that power the next era of progress.

Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, far less efficient, and far more constrained. We're changing that.

Atoms builds Physical AI- real-world robots for the industries that move civilization forward, starting with food, mining, and transport. Our systems are designed to understand, predict, and control the real world with precision, turning complex physical operations into something more reliable, more scalable, and more productive.

This work requires more than robotics. It requires deep integration across hardware, software, AI, operations, manufacturing, and real estate. We don't just build machines in a lab. We deploy them into real environments, operate them, learn from them, and improve them until they work at scale.

We are roboticists, engineers, operators, and builders. We believe the next great technology companies will not only transform information, but the physical systems that shape everyday life.

If you want to work on hard problems with real-world impact, join us.


What we're seeking

A visionary Machine Learning Engineer to join our founding team who will help bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. We are actively hiring across three core specialized subcategories: AI Research, Post-Training Optimization, and Data Engineering.

AI Researcher (World Models & VLA)What you'll do
  • Research and develop cutting edge RL and distillation techniques for trajectory planning
  • Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions
  • Design and deploy end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation
  • Develop interactive world models from raw multi-sensor logs, allowing the team to re-simulate events and query what a vehicle would see if it altered its trajectory
  • Ensure core autonomous driving models can seamlessly adapt to novel urban environments and edge cases
  • Partner with validation and QA teams to run model releases through rigorous simulated scenarios, detecting regressions and identifying systemic performance bottlenecks.
What we're looking for
  • 10+ years of non-internship professional MLE experience.
  • Deep expertise in applying AI Transformers to robotics, physical actuation, or spatial-temporal data.
  • Proven track record designing or training multimodal systems, large-scale VLA models, or generative Diffusion models.
  • Strong background in Sensor Fusion, combining inputs from Cameras, LiDAR, and Radar.
  • Fluency in PyTorch or JAX for training large-scale models.
  • Experience with multi-task learning, Birds-Eye-View (BEV) frameworks, representation learning, or data tokenization is highly preferred.
  • Proficiency in Python and familiarity with C++.
Post-Training & OptimizationWhat you'll do
  • Own the post-training lifecycle by distilling, quantizing, and optimizing massive models to run with low latency on vehicle edge hardware.
  • Profile real-time inference pipelines to identify and eliminate CPU, GPU, and memory bandwidth bottlenecks on the vehicle.
  • Work with low-level hardware, electrical, and firmware teams to iterate on custom carrier boards, sensor interfaces, and GPUs on edge devices.
  • Benchmark and deploy models utilizing hardware-accelerated runtimes (e.g., TensorRT, CUDA) to minimize inference times under strict constraints.
What we're looking for
  • 10+ years of non-internship professional MLE experience.
  • Strong background in machine learning engineering with a focus on model optimization, distillation, and deployment.
  • Hands-on experience optimizing models for edge deployment or custom embedded GPU targets.
  • Deep understanding of profiling tools and debugging resource constraints across CPU/GPU boundaries.
  • Experience with modern deep learning frameworks (PyTorch or JAX) and runtime compilation.
  • Robust programming skills in Python and C++.
  • Familiarity with low-level camera/sensor interfaces and robotics hardware is a significant plus.
Data & Long-Tail ScenariosWhat you'll do
  • Architect automated pipelines to ingest, filter, and identify rare, high-value, and long-tail scenarios out of multi-petabyte multi-sensor datasets.
  • Target and extract complex structural corner cases from real-world driving logs to continuously feed, challenge, and improve our end-to-end behavior models.
  • Iterate closely with QA, testing, and simulation teams to transform ambiguous real-world anomalies into concrete data blocks for simulation testing.
  • Implement programmatic data curation, active learning strategies, and statistical quality metrics to optimize the signal-to-noise ratio of our training pipelines.
What we're looking for
  • 10+ years of non-internship professional MLE experience.
  • Professional experience building data curation pipelines, active learning workflows, or data mining architectures for massive physical datasets.
  • Strong familiarity with robotics data structures and spatial frameworks, including Birds-Eye-View (BEV) or spatial tokenization.
  • Experience processing and structuring raw data from Cameras, LiDAR, and Radar.
  • Expert-level proficiency in Python, data engineering frameworks, and PyTorch/JAX.
  • Exceptional ability to navigate, structure, and derive signal from highly ambiguous, messy, or undefined real-world data distributions.

Why join us

At Atoms, you'll work on one of the defining challenges of our time - bringing automation into the physical world to drive real, lasting impact. We exist to uncover valuable unknown truths and turn them into progress, which means constantly pushing beyond what's known and building what doesn't yet exist. The work is ambitious and often challenging, but it's grounded in a shared sense of purpose and a team committed to seeing it through together. Our work only matters if it serves others, and we know that meaningful progress depends on the trust of the people we serve and the strength of our team-so we invest in both, creating an environment where you can do your best work and grow.

What else you need to know

This role is based in our San Francisco office. Atoms is a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That's why all of our office-based teams work onsite, five days a week.

The base salary range for this role is $273,000 - $345,000 per year.

Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.

Base salary is just one part of your total rewards package. You may also be eligible for equity awards and an annual performance-based bonus.

Benefits Summary (USA Full-Time Exempt Employees):

  • Medical, Dental, Vision, Disability, and Life Insurance
  • Flexible Spending Account / Health Savings Account Options
  • 401(k)
  • Equity
  • Sick Time, Unlimited Flexible Time Off, and Paid Holidays
  • Paid Parental Leave 
  • Pre-Tax Commuter Benefit Plan
  • Team lunch in our SoMa office every Tuesday and Thursday

Benefits are subject to change at the company's discretion.
Atoms accepts applications on an ongoing basis.