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Applied Scientist Machine Learning Jobs in California

Sr. Applied Scientist

San Jose, CA · On-site

$107K - $146K/yr

They are seeking a Senior Applied Scientist to improve the quality and controllability of ... D. in Computer Science, Machine Learning, or a related field preferred. • Proven track record in ...

Applied Scientist (ML)

Mountain View, CA · Hybrid

$190K - $275K/yr

PhD or Master's degree in Computer Science, Machine Learning, NLP, or a related field * Strong ... industry applied ML research environment * Familiarity with retrieval-augmented generation ...

Senior Applied Scientist

San Jose, CA · On-site

$107K - $146K/yr

Senior Applied Scientist - Brand Intelligence Predict The Opportunity Join us at Adobe as a Senior ... MS or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent ...

As an Applied Scientist at Adobe, you will join a world-class team of applied researchers and ... Experience implementing machine learning models using modern deep learning frameworks (e.g ...

Showing results 41-60

Applied Scientist Machine Learning information

See California salary details

$22K

$127K

$199.8K

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

As of Aug 10, 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 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.

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 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.
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:
Infographic showing various Applied Scientist Machine Learning job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $127,000 per year, or $61.1 per hour.

Sr Applied Scientist

Uber Technologies, Inc.

San Francisco, CA • On-site, Remote

Full-time

Retirement

Posted 22 days ago


Uber rating

6.9

Company rating: 6.9 out of 10

Based on 112 frontline employees who took The Breakroom Quiz

4th of 9 rated taxi private hire


Job description

About the role and team

Science and Engineering at Uber mean building for real-world impact under real-world constraints. As a Senior Applied Scientist, you will work at the high-stakes intersection of economics, statistics, and computer science to build the intelligent systems that power our global marketplaces. Unlike purely analytics roles, this is a production-focused position where you will turn "messy" behavioral data into scalable, machine-readable insights and automated decision-making engines.

You will join a high-stakes environment where the work is fast-moving, and the systems you build directly affect how millions of people and things move across our global platforms every single day. Collaborating closely with Product and Engineering, you will lead high-visibility projects from conceptualization to global productionization, navigating technical debt and shifting priorities along the way. If you are a systems-thinker who stays calm under pressure and is motivated to build production-grade models that solve unstructured problems without a textbook solution, this is where you'll grow.

What you'll do

  • Build and deploy production-grade ML models and statistical algorithms that enhance platform intelligence and user experience in real-time environments.
  • Design complex experiments and causal inference frameworks to interpret results and drive trade-offs between short-term wins and long-term system reliability.
  • Architect underlying systems, observability platforms, and automated tooling required to monitor model performance and detect degradations at scale.
  • Solve high-impact problems by translating ambiguous business needs into rigorous mathematical frameworks and production-ready code.
  • Collaborate across Engineering, Product, and Operations to influence technical roadmaps and drive the adoption of scientific best practices.
  • Own your work end-to-end, from identifying raw features and handling data imbalance to debugging production issues when the stakes are high.

Basic Qualifications

  • Minimum 4 years of professional experience as a Machine Learning Scientist, Research Scientist, or Applied Scientist with a record of scoping complex problems independently.
  • Expert proficiency in probability and statistics (e.g., multivariate distributions, sampling) and core optimization techniques (e.g., Gradient Descent, MCMC).
  • Advanced coding proficiency with the ability to contribute to production-level codebases and develop modular tools re-used across teams.
  • Experience performing extensive testing, monitoring, and instrumenting alerting to ensure the reliability of real-time systems.
  • Demonstrated business acumen with the ability to justify technical decisions within a broader strategic business case.
  • Exceptional communication skills with the ability to produce high-impact material for senior audiences and manage meetings with clear objectives.
  • M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, Economics, or another quantitative field (or equivalent professional experience).

Preferred Qualifications

  • Deep domain expertise in developing large-scale intelligent systems that manage supply, demand, or user behavior in a dynamic environment.
  • Experience with Bayesian methods, probabilistic programming (e.g., STAN or Pyro), or advanced reinforcement learning.
  • Demonstrated ability to lead multi-functional projects and navigate extreme ambiguity in a self-guided manner.
  • Grit and a strong sense of ownership, with the ability to deliver on tight timelines while maintaining a high bar for engineering excellence.

Ready to Ride?


This isn't the kind of place where you follow a playbook - it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves - we'd love to hear from you.


You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.


Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.


Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.

For New York City, NY-based roles: The base salary range for this role is USD $190,000 per year - USD $211,000 per year.


For San Francisco, CA-based roles: The base salary range for this role is USD $190,000 per year - USD $211,000 per year.


For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.


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