Staff ML Engineer — Open Models & RL Research

Reflection AI Ltd; New York (NY); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Top-tier compensation

Stock options

Health & wellness

Meals provided

Life & family

Unlimited vacation

Visa sponsorship

Team building

Job summary

Reflection AI Ltd. is hiring to build systems that transform powerful pre-trained models into aligned, general agents. You will drive research and engineering initiatives across data curation, large-scale optimization, and reinforcement learning to push the frontiers of AI.

The role demands deep ML fundamentals, strong software engineering, and a track record of improving model behavior. Collaboration across research and infra, with a bias for action and clear execution, is essential.

Qualifications

• Deep understanding of machine learning fundamentals.
• Strong engineering skills and ability to work in large ML codebases.
• Experience improving model behavior via data, reward modeling, or RL.
• Proven record of ambitious research or engineering agendas with measurable impact.
• Ability to operate across research and infrastructure boundaries.
• Excellent communication and collaboration.
• Passion for advancing the frontier of intelligence.

Responsibilities

• Build systems that transform pre-trained models into aligned, 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 deliver improvements in model capability.
• Contribute to understanding how large models learn to reason and follow instructions.

Skills

ML fundamentals

Distributed systems

LLM training

Data generation

RL techniques

Execution clarity

Job description

Reflection AI Ltd. is hiring to build systems that transform powerful pre-trained models into aligned, general agents. You will drive research and engineering initiatives across data curation, large-scale optimization, and reinforcement learning to push the frontiers of AI.

The role demands deep ML fundamentals, strong software engineering, and a track record of improving model behavior. Collaboration across research and infra, with a bias for action and clear execution, is essential.