Experience with RL post-training (RLHF, GRPO, tool-augmented RL) * Experience training MoE architectures Location San Francisco, CA or Cambridge, MA (Remote, Hybrid, and On-Site available depending ...
Experience with RL post-training (RLHF, GRPO, tool-augmented RL) * Experience training MoE architectures Location San Francisco, CA or Cambridge, MA (Remote, Hybrid, and On-Site available depending ...
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?
| Aspect | Remote Rlhf | Remote Rlhf |
|---|---|---|
| Credentials | Typically requires certification in mental health or counseling, such as LPC or LCSW | Similar credentials, often with additional training in specific therapy methods |
| Work Environment | Remote, client-facing sessions via telehealth platforms | Remote, providing therapy or support services online |
| Industry Usage | Common in mental health, therapy, and counseling sectors | Used 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.
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Posted 22 days ago
Job description
Your Impact at LILA
The AI Research team is tackling one of the most exciting, open problems in AI: training LLMs to run long-horizon scientific discovery tasks. Our approach spans the full post-training stack - from SFT to asynchronous RL on agentic harnesses - teaching models to plan, use tools, and learn from experience in domains where the ground truth isn't a preference label, but a scientific result.
We're rapidly growing our Research Engineering org and seeking talented engineers and ML practitioners across levels to design, build, and optimize systems to push this frontier: scaling post-training, sharpening reasoning, and unlocking compute-intensive agentic-harness training. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems.
We operate with high agency, and a bias toward execution. Below are several focus areas within the team. We ask that candidates select the stream that best matches their experience and excitement.
Work Streams
Stream A: GPU Optimization & Training Performance
Maximize hardware utilization across 100B+ parameter asynchronous RL training runs. Responsibilities include profiling, performance optimization, custom kernel development, communication-computation overlap, and long-context throughput improvements. You set and maintain the performance baseline.
Stream B: Stack & Infrastructure
Own the post-training infrastructure end-to-end - supervised fine-tuning, asynchronous RL with tool integration, and data pipelines. Build modular, reproducible workflows with single-command execution. Manage upstream framework upgrades and deliver composable pipelines spanning Data, SFT, and RL stages. You work tightly with Research Scientists to develop and productionize novel algorithms to run at scale.
Stream C: Model Experimentation
Bring deep, hands-on experience training large language models. Lead experimentation on reasoning model development, including mixture-of-experts stabilization, curriculum design, and synthetic reasoning trace generation. You have a bias toward experimental design and tracking, and know how to prioritize runs that yield promising outcomes.
Stream D: Evaluations & Benchmarks
Design and build best-in-class scientific agentic benchmarks and harnesses, along with the dashboards and leaderboards that inform every training decision. You have experience working with well known public benchmarks and have spent time building bespoke agentic benchmarks and harnesses.
Stream E: Agentic Capabilities & Frontier Research
Train models capable of planning, exploration, and tool use over extended horizons. Advance the state of the art in RL at scale with tool-calling, subgoal decomposition, and shared memory/skills across trials to expand the frontier of scientific agent capabilities.
What You'll Need to Succeed
- Strong software engineering skills in Python; C++/CUDA a plus
- Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray)
- Understanding of large-scale model training techniques for 100B+ models
- Experience with cloud or HPC environment
- Ability to communicate technical results to internal and external stakeholders
Bonus Points For
- Prior work with large scale scientific datasets or domain-specific modeling
- Contributions to open-source ML frameworks
- Experience with RL post-training (RLHF, GRPO, tool-augmented RL)
- Experience training MoE architectures
Location
San Francisco, CA or Cambridge, MA (Remote, Hybrid, and On-Site available depending on team needs).