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Helper Reinforcement Learning Jobs in California

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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.

What are the most commonly searched types of Reinforcement Learning jobs in California?

The most popular types of Reinforcement Learning jobs in California are:

What job categories do people searching Helper Reinforcement Learning jobs in California look for?

The top searched job categories for Helper Reinforcement Learning jobs in California are:

What cities in California are hiring for Helper Reinforcement Learning jobs?

Cities in California with the most Helper Reinforcement Learning job openings:

Research Engineer, Reinforcement Learning

San Francisco, CA • On-site

Full-time

Re-posted 21 days ago


Job description

Job Summary:
TensorStax is building fully autonomous AI systems to manage and maintain mission-critical data infrastructure and pipelines. They are seeking a Research Engineer specializing in Reinforcement Learning to develop and refine reward functions, create RL gym environments, and fine-tune language models using advanced reinforcement learning techniques.
Responsibilities:
• Develop and refine reward functions to optimize agent behavior for complex data engineering tasks.
• Create RL gym environments for language model agents.
• Fine-tune language models using reinforcement learning techniques such as PPO, DPO, and KTO.
• Stay at the forefront of research on RL for language models, incorporating advancements like GRPO, SWE-Gym, and SWE-RL into practical applications.
• Curate and build high-quality datasets for supervised fine-tuning (SFT) and RLHF.
• Design experiments to evaluate and improve the agentic capabilities of language models in data environments.
Qualifications:
Required:
• Deep understanding of reinforcement learning, reward shaping, and optimization strategies.
• Strong familiarity with LLM fine-tuning techniques (PPO, DPO, KTO) and their applications in reinforcement learning.
• Knowledge of recent advancements in RL for language models (GRPO, SWE-Gym, SWE-RL).
• Experience curating and constructing high-quality datasets for fine-tuning.
• Strong problem-solving skills and a history of working on complex ML projects.
• High agency—ability to work independently, experiment proactively, and drive research initiatives forward.
Preferred:
• Experience with distributed training in PyTorch (DDP, FSDP).
• Hands-on experience designing RL environments for traditional RL problems.
• Contributions to open-source projects in RL, LLMs, or ML infrastructure.
• Familiarity with data lakes and warehouses (Snowflake, BigQuery, Redshift).
Company:
Autonomous AI to help build and maintain data pipelines using your infrastructure. Founded in 2024, the company is headquartered in San Francisco, USA, with a team of 2-10 employees. The company is currently Early Stage.