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Applied Scientist Machine Learning Jobs in California

Apple Inc.

Applied Scientist

Culver City, CA · On-site

Austin, Texas, United States Machine Learning and AI Services at Apple help hundreds of millions of ... Description As an Applied Scientist, you will have the responsibility of pushing the boundaries of ...

Meta

Research Scientist, Machine Learning

Sunnyvale, CA · On-site

$122K/yr

Currently has, or is in the process of obtaining, a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or a related technical field. Degree ...

Meta

Research Scientist, Machine Learning

Sunnyvale, CA · On-site

$122K - $181K/yr

Degree must be completed prior to joining Meta • Currently has, or is in the process of obtaining, a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied ...

Rule14

Data Scientist Machine Learning

Santa Monica, CA · On-site

$75K - $100K/yr

As a Data Scientist Machine Learning, you will work within a small data science team focusing on predictive modeling, natural language processing, computer vision, recommender systems, and OCR ...

Thomson Reuters

Applied Scientist Intern

San Francisco, CA · On-site

$17.75 - $23.50/hr

Applied Scientist Intern The dates for this internship are January 11, 2027-July 12, 2027 ... Are you a curious and open-minded individual with an interest in programming and machine learning?

THOMSON REUTERS

Applied Scientist Intern

San Francisco, CA · On-site

$17.75 - $23.50/hr

Applied Scientist Intern The dates for this internship are January 11, 2027-July 12, 2027 ... Are you a curious and open-minded individual with an interest in programming and machine learning?

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How much do applied scientist machine learning jobs pay per year?

As of Sep 13, 2026, the average yearly pay for applied scientist machine learning in California is $127,000.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,391.00 and $154,718.00 per year, depending on experience, location, and employer.

What does an applied scientist in machine learning do?

An Applied Scientist in Machine Learning develops and implements machine learning models to solve real-world problems. They work on collecting and preprocessing data, designing algorithms, and evaluating model performance. Their work often bridges research and product development, collaborating with engineers and data scientists to deploy solutions in production. Applied Scientists also keep up-to-date with the latest advancements in machine learning to continuously improve systems and outcomes.

How does an applied scientist in machine learning typically collaborate with software engineers and data engineers on projects?

Applied Scientists in Machine Learning often work closely with software engineers and data engineers to bring machine learning models from prototype to production. They usually develop and validate models, while data engineers assist in preparing and managing large datasets, and software engineers help integrate models into scalable applications. Effective communication and cross-functional teamwork are essential, as the role requires translating scientific findings into practical solutions that align with business goals. Regular meetings, code reviews, and collaborative problem-solving sessions are common, ensuring smooth transitions between research and deployment phases.

What are the key skills and qualifications needed to thrive as an applied scientist in machine learning, and why are they important?

To thrive as an Applied Scientist in Machine Learning, you need a solid background in mathematics, statistics, computer science, and typically a master's or PhD in a related field. Proficiency in programming languages like Python or Java, experience with ML frameworks such as TensorFlow or PyTorch, and familiarity with cloud platforms and data processing tools are crucial. Strong problem-solving skills, intellectual curiosity, and the ability to communicate complex ideas clearly make candidates stand out. These skills ensure effective development, implementation, and communication of advanced machine learning solutions that drive business impact.

What are the most commonly searched types of Applied Scientist Machine Learning jobs in California?

The most popular types of Applied Scientist Machine Learning jobs in California are:

What are popular job titles related to Applied Scientist Machine Learning jobs in California?

For Applied Scientist Machine Learning jobs in California, the most frequently searched job titles are:

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The top searched job categories for Applied Scientist Machine Learning jobs in California are:

Applied Scientist Machine Learning jobs near you
Infographic showing various Applied Scientist Machine Learning job openings in California as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 73% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $127,000 per year, or $61.1 per hour.

Applied Scientist / Machine Learning Engineer

Sunnyvale, CA • On-site

Wayve
See more jobs at Wayveexternal

Full-time

Re-posted 14 days ago

Job description

Wayve is building embodied AI for the physical world, starting with autonomous driving. Instead of the hand-engineered, modular stacks that defined the first era of self-driving, we pioneered AV2.0: a single, end-to-end neural network that learns to drive from raw sensor data and generalises to new cities, vehicles, and conditions. Our foundation models, the GAIA family of generative world models and the LINGO family of vision-language-action models, let vehicles perceive, reason, and act in the open world. We have driven zero-shot across hundreds of cities on three continents, and we are now scaling from proving the science to deploying it with leading automakers and mobility partners, including Nissan, Stellantis, and Uber.
The role
This role sits in the AI Platform organisation, on the data flywheel that powers every model we ship. The thesis is simple and compounding: the more intelligently we curate, enrich, and evaluate the real-world driving experience our fleet generates, the faster our foundation models improve, and the further they generalise across geographies, embodiments, and OEM platforms. As deployment scales, the bottleneck is shifting from raw model capacity to the quality and intelligence of the data engine and the rigour of how we measure progress. That is the problem you will own.
This is adual-track role: we are hiring at either Applied Scientist or Machine Learning Engineer, at TC3 (Senior) or TC4 (Staff / Tech Lead), calibrated to your background. We are open on specialisation. There are three areas we are hiring into, and you can go deep in any one of them:
  • Data curation: mine world-scale fleet data for the rare, long-tail, and safety-critical moments that move the model.
  • Data enrichment: turn raw driving experience into high-signal training data through (semi-)automated enrichment, labeling, and data quality at scale.
  • Foundation model evaluation: define how we know a driving foundation model is genuinely getting better, offline and in closed loop.

Day to day, the role also spans the broader foundation-model stack, including vision-language-action and vision-language models for embodied AI, world modeling, policy learning, reinforcement learning, and reward modeling.
Key responsibilities
  • Mine world-scale fleet data for rare, long-tail, and safety-critical events using active learning, smart sampling, and embedding-based retrieval and dedup.
  • Figure out what makes a good training dataset: which data, mix, and balance actually move the model, and turn that into repeatable curation across cities, sensor rigs, and embodiments.
  • Build high-quality enrichments that teams across the company depend on, through (semi-)automated enrichment and labeling pipelines and data quality at scale.
  • Build and fine-tune large-scale pretrained models, and run smaller-scale experiments to test and derisk ideas before committing serious compute.
  • Help build the best embodied VLM / VLA in the world for driving (the LINGO line): push multimodal perception, reasoning, language, and action.
  • Design rigorous offline and closed-loop evaluation: metrics and benchmarks that correlate with real on-road behaviour and safety, with deliberate coverage of rare and safety-critical scenarios.
  • Use world-model-based evaluation (GAIA) to probe counterfactual "what if" scenarios safely, repeatably, and at scale.
  • Contribute across the wider foundation-model stack as the work demands: generative world models (GAIA), policy learning, reinforcement learning, and reward modeling.

About you
Essential
  • A Masters with around 6 or more years of relevant experience, or a PhD with 2 or more years, in computer science, machine learning, robotics, mathematics, or a related field (required).
  • Strong ML and software fundamentals, and a track record of taking ML from research into production systems that run at scale.
  • Hands-on strength in one or more of: data curation, foundation model training, large-scale data wrangling, and foundation-model evaluation (for example, evaluation of LLMs or similar large models).
  • Experience with large-scale data and/or large neural networks, and the judgment to know which experiments and which data actually matter.
  • Fluency in Python and a modern deep-learning framework (PyTorch or similar), and comfort working with large, messy, real-world datasets.

Desirable
  • Autonomous driving, robotics, or other embodied-AI domains.
  • Foundation models, VLMs, world models, diffusion or autoregressive generative models, or reinforcement learning and reward modeling.
  • Large-scale data infrastructure: embedding and vector search (e.g. turbopuffer, Milvus), distributed data processing (Ray Data, Daft, Spark), lakehouse formats (Lance, Iceberg), or annotation tooling.
  • Closed-loop or simulation-based evaluation, and safety-critical ML.
  • Publications at top ML, CV, or robotics venues (NeurIPS, ICML, ICLR, CVPR, CoRL, RSS).

This is a full-time role based in our office in Sunnyvale. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. The reasonably estimated salary for this role ranges from $311,850 to $370,000, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.

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