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Helper Reinforcement Learning Jobs in California

Spotter

Machine Learning Scientist

Culver City, CA · Remote

Working alongside Analytics, Product, and Engineering, you'll help develop intelligent systems that ... Applying reinforcement learning, contextual bandits, online learning, and other adaptive learning ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

Working alongside Analytics, Product, and Engineering, you'll help develop intelligent systems that ... Applying reinforcement learning, contextual bandits, online learning, and other adaptive learning ...

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Helper Reinforcement Learning information

What is the difference between Helper Reinforcement Learning vs Data Scientist?

AspectHelper Reinforcement LearningData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; knowledge of reinforcement learningDegree in Data Science, Statistics, Computer Science; proficiency in programming and analytics
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, consulting firms, tech companies
Industry UsageAI development, machine learning projectsData analysis, predictive modeling, business insights
Common Search/ComparisonHelper Reinforcement Learning vs Data Scientist

Helper Reinforcement Learning focuses on developing algorithms that enable machines to learn through interactions, often requiring knowledge of reinforcement learning techniques. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve programming and data handling, Helper Reinforcement Learning is more specialized in AI algorithm development, whereas Data Scientists work broadly across data analysis and modeling in various industries.

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Helper Reinforcement Learning jobs near you
Research Engineer, Machine Learning (Reinforcement Learning)

Research Engineer, Machine Learning (Reinforcement Learning)

Anthropic

San Francisco, CA

$241K/yr

Other

Posted 20 days ago

Job description

About the teams

Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas:

  • Developing systems that enable models to use computers effectively
  • Advancing code generation through reinforcement learning
  • Pioneering fundamental RL research for large language models
  • Building scalable RL infrastructure and training methodologies
  • Enhancing model reasoning capabilities

We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.

About the Role

As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation.

Representative projects:
  • Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows.
  • Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models.
  • Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows.
  • Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research.
You may be a good fit if you:
  • Are proficient in Python and async/concurrent programming with frameworks like Trio
  • Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX)
  • Have industry experience in machine learning research
  • Can balance research exploration with engineering implementation
  • Enjoy pair programming (we love to pair!)
  • Care about code quality, testing, and performance
  • Have strong systems design and communication skills
  • Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems
Strong candidates may have:
  • Familiarity with LLM architectures and training methodologies
  • Experience with reinforcement learning techniques and environments
  • Experience with virtualization and sandboxed code execution environments
  • Experience with Kubernetes
  • Experience with distributed systems or high-performance computing
  • Experience with Rust and/or C++
Strong candidates need not have:
  • Formal certifications or education credentials
  • Academic research experience or publication history

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

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