ZipRecruiter

Log In

2

Remote Rlhf Jobs in California (NOW HIRING)

Invoca

Senior ML Engineer

San Francisco, CA · On-site +1

$123.10K - $169.10K/yr

Familiarity with RLHF or preference training is a bonus 📍 Location This is a remote-first role. We are currently hiring in the following locations: 📍 United States: Greater Los Angeles Area ...

Snorkel AI

Senior Manager - Research

San Francisco, CA · On-site +1

$250K - $350K/yr

Anticipate the next bottlenecks in the AI data (e.g., automated RLHF, agentic evaluation, or ... San Francisco, CA or Remote Reports to: Head of Research (Co-founder) Salary Range $250,000-$350 ...

Invoca

Senior ML Engineer

Anaheim, CA · On-site +1

$109.40K - $150.20K/yr

Familiarity with RLHF or preference training is a bonus 📍 Location This is a remote-first role. We are currently hiring in the following locations: 📍 United States: Greater Los Angeles Area ...

next page

Showing results 1-20

People also search for

All JobsRlhf JobsRemote Rlhf JobsRemote Rlhf Jobs in California

Remote Rlhf information

What are the key skills and qualifications needed to thrive as a Remote RLHF (Reinforcement Learning from Human Feedback) Engineer, and why are they important?

To succeed as a Remote RLHF Engineer, you need expertise in machine learning, reinforcement learning, and programming languages like Python, often supported by an advanced degree in computer science or related fields. Familiarity with ML frameworks (such as TensorFlow or PyTorch), version control systems, and cloud computing platforms is typically required. Strong problem-solving, communication, and self-management skills are vital for remote collaboration and interpreting human feedback effectively. These skills enable the development of robust AI systems that can learn efficiently from human input while ensuring productive teamwork in a distributed environment.

How does a Remote RLHF (Reinforcement Learning from Human Feedback) specialist typically collaborate with other team members?

A Remote RLHF specialist often works closely with data scientists, machine learning engineers, and product managers to design and refine AI models using human feedback. Collaboration usually happens through regular virtual meetings, cloud-based code repositories, and shared annotation tools. The role requires clear communication to ensure that human feedback is accurately integrated into the learning process and that model improvements align with project goals. Being proactive in sharing findings and challenges is key, as team members may be distributed across different time zones.

What is a Remote RLHF job?

A Remote RLHF (Reinforcement Learning from Human Feedback) job involves working with artificial intelligence systems, particularly large language models, to improve their performance using feedback from humans. In this role, individuals may annotate data, provide quality evaluations, or help design feedback mechanisms while working from a remote location. These jobs are crucial for ensuring AI models align better with human values and expectations, and they are often offered by AI research companies or organizations focused on machine learning. The work can involve tasks such as ranking AI-generated responses, identifying errors, and suggesting improvements. Remote RLHF positions are popular due to their flexibility and the opportunity to contribute to cutting-edge AI technology.

What is the difference between Remote Rlhf vs Remote Rlhf?

AspectRemote RlhfRemote Rlhf
CredentialsTypically requires certification in mental health or counseling, such as LPC or LCSWSimilar credentials, often with additional training in specific therapy methods
Work EnvironmentRemote, client-facing sessions via telehealth platformsRemote, providing therapy or support services online
Industry UsageCommon in mental health, therapy, and counseling sectorsUsed in mental health and support services, often interchangeably with Rlhf

Remote Rlhf and Remote Rlhf are similar roles in mental health support, primarily differing in specific certifications or training focus. Both roles involve providing remote therapy or support services via telehealth platforms, making them highly comparable in work environment and industry usage.

What are the most commonly searched types of Rlhf jobs in California? The most popular types of Rlhf jobs in California are:
What are popular job titles related to Remote Rlhf jobs in California? For Remote Rlhf jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Rlhf jobs in California look for? The top searched job categories for Remote Rlhf jobs in California are:
What cities in California are hiring for Remote Rlhf jobs? Cities in California with the most Remote Rlhf job openings:
Remote Rlhf jobs near you
Infographic showing various Remote Rlhf job openings in California as of May 2026, with employment types broken down into 17% Internship, and 83% Full Time. Highlights an 100% Remote job distribution.

Applied Research - Evals & Data

Prime Intellect

San Francisco, CA • On-site, Remote

$150/hr

Other

Posted 7 days ago

Job description

Be Your Own Lab
Prime Intellect builds the infrastructure that frontier AI labs build internally, and makes it available to everyone. Our platform, Lab, unifies environments, evaluations, sandboxes, and high-performance training into a single full-stack system for post-training at frontier scale, from RL and SFT to tool use, agent workflows, and deployment. We validate everything by using it ourselves, training open state-of-the-art models on the same stack we put in your hands. We're looking for people who want to build at the intersection of frontier research and real infrastructure.
We recently raised $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.
Role Impact
This is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems. You'll have a direct impact on shaping how advanced models are aligned, evaluated, deployed, and used in the real world by:
  • Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads-workflow automation, reasoning-intensive tasks, and decision-making at scale. Working with applied data from real deployments to continuously refine policies, improve reasoning, and enhance reliability and safety.
  • Building Robust Infrastructure: Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale. Building data capture, processing, and versioning workflows for feedback, model traces, and reward signals.
  • Bridge Between Customers & Research: Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities. Collaborating closely with RL and eval teams to ensure real-world signals inform model alignment and reward shaping.
  • Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses alongside customers to validate solutions. Using applied evaluation data to iterate on model performance and discover new capabilities.
Customer-Facing Engineering
  • Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks.
  • Prototype agents, data pipelines, and eval harnesses tailored to real use cases, then hand off hardened systems to core teams.
  • Translate customer insights and evaluation results into roadmap and research direction.
Post-training & Reinforcement Learning
  • Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.
  • Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior in real-world workflows.
  • Integrate applied data collection and analytics into the post-training process to surface regressions, emergent skills, and alignment opportunities.
  • Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions.
Agent Development & Infrastructure
  • Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.
  • Extend and integrate with agent frameworks to support evolving feature requests and performance requirements.
  • Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency.
  • Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments.
Requirements
  • Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.
  • Experience with applied data workflows and evaluation frameworks for large models or agents (e.g., SWE-Bench, HELM, EvalFlow, internal eval pipelines).
  • Deep expertise in distributed training/inference frameworks (e.g., vLLM, sglang, Ray, Accelerate).
  • Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform).
  • Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL.
  • Passion for advancing the state-of-the-art in reasoning, measurement, and building practical, agentic AI systems.
What We Offer
  • Cash Compensation Range of $150-300k + equity incentives
  • Flexible Work (remote or San Francisco)
  • Visa Sponsorship & relocation support
  • Professional Development budget
  • Team Off-sites & conference attendance
Growth Opportunity
You'll join a mission-driven team working at the frontier of open, superintelligence infra. In this role, you'll have the opportunity to:
  • Shape the evolution of agent-driven, data-informed solutions-from research breakthroughs to production systems used by real customers.
  • Collaborate with leading researchers, engineers, and partners pushing the boundaries of RL, evaluation, and post-training.
  • Grow with a fast-moving organization where your contributions directly influence both the technical direction and the broader AI ecosystem.

If you're excited to move fast, build boldly, and help define how agentic AI is developed and deployed, we'd love to hear from you.
Ready to build the open superintelligence infrastructure of tomorrow?
Apply now to help us make powerful, open AGI accessible to everyone.

Apply