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Remote Rlhf Jobs in California (NOW HIRING)

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Remote Rlhf information

What is a remote RLHF?

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.

How does a remote RLHF 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 are the key skills and qualifications needed to thrive as a remote RLHF 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.

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 August 2026, with employment types broken down into 90% Full Time, and 10% Part Time. Highlights an 100% Remote job distribution.

Machine Learning Engineer II, Visual AI

Pinterest

San Francisco, CA • On-site, Remote

$114K - $157K/yr

Full-time

Re-posted 10 days ago

Job description

The Pinterest Labs group is dedicated to the development and research of applied machine learning. Our initiatives span a diverse range of AI/ML fields, including fundamental computer vision, multimodal large language models, multimodal representation learning, generative modeling, heterogeneous graph neural networks, and recommender systems. By building foundation ML models that utilize our extensive knowledge graph and billions of Pins, we aim to significantly enhance the core Pinterest product.

Our visual modeling team is currently seeking new members to focus on the advancement of vision-centric LLMs. We are building VLMs capable of perceiving intricate visual details and understanding user aesthetics to facilitate communication through visual assets using tools like multimodal search and text-to-image models. This role offers the opportunity to work with Pinterest's unique visual-text datasets to develop large-scale generative models for production. You will join the core visual pod, a collaborative group of approximately six engineers and a product prototyping team, to create specialized evaluation benchmarks and contribute to the broader research community.

What you'll do:

  • Prototype new model architectures for Pinterest VLMs. We're looking for hands-on experience working with finetuning open-source LLM models and improve their visual perception and tool using capabilities.
  • Develop new evaluation benchmarks that tailors to vision-centric capabilities such as fashion style recommendations.
  • Read research papers, participate in group discussions, and help brainstorm our overall visual generative strategy at the company.
  • Help with collection of relevant visual training data for Pinterest Canvas, particularly to conduct RLHF, targeted fine-tuning, etc.
  • Publish and publicize your work via conferences, paper submissions, blog posts, etc. 


What we're looking for:

  • Research engineers and scientists who have experience working with generative computer vision models, preferably various forms of visual encoders and LLMs.
  • 2+ years of industry computer vision experience.
  • M.S. or PhD in Machine Learning, Computer Science, or related areas.


Nice to Have:

  • Publications at top ML conferences.
  • Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
  • Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.

In-Office Requirement Statement:

  • We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
  • This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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