Staff AI Engineer - LLM Systems & RL

Socket.dev; San Francisco (CA); On-site; USD 180,000 - 300,000; Full time

Benefits offered by this job

Top-tier compensation

Health & wellness

Life & family benefits

Benefits & balance

Team connect & off-sites

Job summary

Socket.dev is seeking a research-minded ML engineer to build systems that turn powerful pre-trained models into aligned, general agents. You will drive research and engineering initiatives across data curation, reward modeling, and RL at scale, collaborating across teams to push post-training capabilities.

You should have a deep understanding of ML fundamentals, practical experience with large-scale LLM training, and a track record of delivering improvements.

Qualifications

• Deep understanding of machine learning fundamentals.
• Practical experience with large-scale LLM training and distributed systems.
• Experience improving model behavior via data, rewards, or RL techniques.
• Evidence of owning ambitious research or engineering agendas that led to improvements.
• Thrives in fast-paced startup environments with clear execution.

Responsibilities

• Build systems that transform powerful pre-trained models into aligned and general agents.
• Drive research and engineering initiatives from data curation to large-scale optimization.
• Develop data generation pipelines, reward models, RL algorithms, and inference-time scaling techniques.
• Collaborate across pre-training and post-training teams to achieve step-function gains in model capability.
• Contribute to understanding how large models learn to reason and follow instructions through RL.

Skills

ML fundamentals

Large-scale training

Distributed systems

RL techniques

Research leadership

Communication

Action & execution

Job description

Socket.dev is seeking a research-minded ML engineer to build systems that turn powerful pre-trained models into aligned, general agents. You will drive research and engineering initiatives across data curation, reward modeling, and RL at scale, collaborating across teams to push post-training capabilities.

You should have a deep understanding of ML fundamentals, practical experience with large-scale LLM training, and a track record of delivering improvements.