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Manager Remote Machine Learning Jobs in California

Help build a first-class machine learning platform from the ground up, which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving ...

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk ...

Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk ...

Drive data excellence through hands-on analysis of training and evaluation data, managing noise ... Open to remote candidates or 3 days a week hybrid from our Toronto or San Francisco hubs. Notice to ...

Senior Machine Learning Scientist

San Jose, CA · Remote

$107K - $146K/yr

Manage project scope, timelines, and communication, proactively surfacing risks and trade-offs. * Mentor junior scientists and engineers on modeling approaches, experimentation, and analytical ...

Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning products * Take ownership of ...

Our Machine Learning and Data Science team are growing! We are looking to hire researchers and data ... Partner closely with product managers, engineers, and business stakeholders to understand ...

Our platform manages millions of devices across multiple operating systems, requiring exceptional performance, scalability, availability, and resilience. You will join the AI Platform Team , the ...

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Manager Remote Machine Learning information

What is the difference between Manager Remote Machine Learning vs Data Scientist?

AspectManager Remote Machine LearningData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; leadership experienceBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentRemote team management, project oversightData analysis, model development, research
Employer & Industry UsageTech companies, AI firms, startupsTech, finance, healthcare, research institutions
Common Search & ComparisonYesYes

The main difference is that a Manager Remote Machine Learning oversees ML projects and teams remotely, focusing on leadership and strategy, while a Data Scientist primarily conducts data analysis and model development. Managers handle project management and team coordination, whereas Data Scientists focus on technical implementation and research.

What are the most commonly searched types of Remote Machine Learning jobs in California? The most popular types of Remote Machine Learning jobs in California are:
What job categories do people searching Manager Remote Machine Learning jobs in California look for? The top searched job categories for Manager Remote Machine Learning jobs in California are:
What cities in California are hiring for Manager Remote Machine Learning jobs? Cities in California with the most Manager Remote Machine Learning job openings:
Infographic showing various Manager Remote Machine Learning job openings in California as of June 2026, with employment types broken down into 1% As Needed, 51% Full Time, 45% Part Time, and 3% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Roku

San Jose, CA • Remote

$229K - $367K/yr

Other

Medical, Life, PTO

Re-posted 6 days ago


Job description

About the team 

The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and Roku. The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across large-scale distributed systems with low latency. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation, and Inference Platform, which powers the entire landscape, and we continuously evolve.

About the role 

We're on a mission to build cutting-edge advertising technology that empowers businesses to run sustainable and highly profitable campaigns. The Ad Performance team owns server technologies, data, and cloud services designed to improve the ad experience. We're looking for seasoned engineers with machine learning backgrounds to support this mission. Examples of problems include improving ad relevance, inferring demographics, optimizing yield, and more. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning using both general-purpose software and statistical languages.

For California Only - The estimated annual salary for this position is between $229,500 - $367,100 annually. Compensation packages are based on factors unique to each candidate, including but not limited to skill set, certifications, and specific geographical location. This role is eligible for health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off. 

What you'll be doing 
  • ML infrastructure: Help build a first-class machine learning platform from the ground up, which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving/evaluation, and monitoring prediction quality
  • Data analysis and feature engineering: Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models
  • Model training with batch and real-time prediction scenarios: Use machine learning and statistical modeling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving product/system performance, quality, and accuracy
  • Production operations: Low-level systems debugging, performance measurement, and optimization on large production clusters
  • Collaboration with cross-functional teams: Partner with product managers, data scientists, and other engineers to deliver impactful solutions
  • Staying ahead of the curve: Continuously learn and adapt to emerging technologies and industry trends
We're excited if you have 
  • Bachelor's, Master's, or PhD in Computer Science, Statistics, or a related field
  • 5 years of experience in applied machine learning on real use cases 
  • Proficient coding skills and strong software development experience in Spark, Python, or Java
  • Familiarity with real-time evaluation of models with low latency constraints
  • Familiarity with distributed ML frameworks such as Spark-MLlib, TensorFlow, etc.
  • Ability to work with large-scale computing frameworks, data analysis systems, and modeling environments i.e., Spark, Hive, NoSQL stores such as Aerospike and ScyllaDB
  • Ad Tech experience is preferred 
  • Proficient use of AI tools and agentic coding practices
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