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Reinforcement Learning With Human Feedback Jobs in California

Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback). **No C2C resumes are considered** Thank you! FocusKPI Hiring Team Founded in 2010, FocusKPI, Inc. (FocusKPI ...

Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback). **No C2C resumes are considered** Thank you! FocusKPI Hiring Team Founded in 2010, FocusKPI, Inc. (FocusKPI ...

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Reinforcement Learning With Human Feedback information

What is reinforcement learning with human feedback?

Reinforcement Learning with Human Feedback (RLHF) is a machine learning technique where AI agents are trained not only through automated reward signals but also by incorporating feedback from humans. This approach helps align the agent’s behavior with human preferences, values, or safety requirements by allowing humans to guide or correct the learning process. RLHF is commonly used in developing advanced AI systems, such as language models, to ensure their outputs are helpful, safe, and aligned with user expectations. The process often involves human evaluators ranking or scoring the AI's responses, which are then used to fine-tune the model’s behavior.

What collaborations are typical for a reinforcement learning with human feedback specialist within a machine learning team?

As an RLHF specialist, you often work closely with data scientists, machine learning engineers, and domain experts to design effective feedback mechanisms and reward models. Collaboration with annotation teams or subject matter experts is common, as high-quality human feedback is crucial for training robust RLHF models. You may also partner with product managers and UX researchers to ensure that the models align with user needs and ethical considerations. Regular cross-functional meetings and code reviews help maintain alignment and foster innovation across teams.

What are the key skills and qualifications needed to thrive as a reinforcement learning with human feedback engineer?

To excel as a Reinforcement Learning with Human Feedback (RLHF) Engineer, you need a strong background in machine learning, reinforcement learning theory, statistics, and typically an advanced degree in computer science or a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like Ray RLlib), and experience with data collection and annotation systems are essential. Excellent problem-solving abilities, communication skills, and teamwork help you collaborate with researchers, data annotators, and other engineers. These skills enable you to design and implement RLHF systems that are robust, scalable, and aligned with human values.

What is the difference between Reinforcement Learning With Human Feedback vs Reinforcement Learning Engineer?

AspectReinforcement Learning With Human FeedbackReinforcement Learning Engineer
CredentialsTypically requires knowledge of machine learning, AI, and data analysisRequires similar credentials in machine learning, programming, and AI
Work EnvironmentResearch labs, AI development teams, tech companiesDevelopment teams, research labs, tech firms
Industry UsageUsed in AI training, human-in-the-loop systems, and model refinementDesigning, implementing, and optimizing reinforcement learning algorithms

Reinforcement Learning With Human Feedback focuses on improving AI models through human input, while Reinforcement Learning Engineers develop and deploy these algorithms. Both roles require strong machine learning skills and often work in similar environments, but their core responsibilities differ in application and focus.

What are popular job titles related to Reinforcement Learning With Human Feedback jobs in California?

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What job categories do people searching Reinforcement Learning With Human Feedback jobs in California look for?

The top searched job categories for Reinforcement Learning With Human Feedback jobs in California are:

What cities in California are hiring for Reinforcement Learning With Human Feedback jobs?

Cities in California with the most Reinforcement Learning With Human Feedback job openings:

Infographic showing various Reinforcement Learning With Human Feedback job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

LLM Research Engineer

Mountain View, CA

Cypress HCM
Recruiting and Staffing Services • 51 - 200 employees

$90 - $121.86/hr

Full-time

Re-posted 13 days ago


Job description


LLM Research Engineer
Key Responsibilities:
  • Design, train, and fine-tune large language models (e.g., GPT, LLaMA, PaLM) for various applications.
  • Conduct research on cutting-edge techniques in natural language processing (NLP) and machine learning to improve model performance.
  • Explore advancements in transformer architectures, multi-modal models, and emergent AI behaviors.
  • Collect, clean, and preprocess large-scale text datasets from diverse sources.
  • Develop and implement data augmentation techniques to improve training data quality.
  • Ensure data is free from bias and aligned with ethical AI standards.
  • Optimize model architecture to improve accuracy, efficiency, and scalability.
  • Implement techniques to reduce latency, memory footprint, and inference time for real-time applications.
  • Collaborate with MLOps teams to deploy LLMs into production environments using Docker, Kubernetes, and cloud
  • Develop robust evaluation pipelines to measure model performance using key metrics like accuracy, perplexity, BLEU, and F1 score.
  • Continuously test for bias, fairness, and robustness of language models across diverse datasets.
  • Conduct A/B testing to evaluate model improvements in real-world applications.
    Stay updated with the latest advancements in generative AI, transformers, and NLP research.
  • Contribute to research papers, patents, and open-source projects.
  • Present findings and insights at conferences and internal knowledge-sharing sessions.
Qualifications:
  • 7-10 years experience
  • Advanced degree in CS, Artificial Intelligence, Data Science, or a related field.
  • Strong programming skills.
  • Proficiency with deep learning frameworks such as TensorFlow, PyTorch, or JAX.
  • Hands-on experience with transformer-based models (e.g., GPT, BERT, RoBERTa, LLaMA).
  • Expertise in natural language processing (NLP) and sequence-to-sequence models.
  • Familiarity with Hugging Face libraries and OpenAI APIs.
  • Experience with MLOps tools like Docker, Kubernetes, and CI/CD pipelines.
  • Strong understanding of distributed computing and GPU acceleration using CUDA.
  • Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback).
Compensation: $90 - $121.86 per hour
ID#: 36408719

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About Cypress HCM

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We deliver consistently superior recruiting by virtue of trusting, communicative relationships with companies and candidates alike. From Fortune 100s to startups, clients lean on us to fulfill their range of needs from contract to full-time positions. With an intimate knowledge of the industries we serve, a keen sense of what makes for high-performing talent in any role, and shared sense of urgency, our clients will tell you: your solution begins here.

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

Walnut Creek, CA, US

Year founded

2005

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