Founding Research Engineer, Model Training

About the role

This role sits at the boundary of research and engineering. You will work on training, post-training, evaluation, performance optimization, and the systems needed to support all of that.

Responsibilities

• Build and improve training and post-training systems for biological foundation models and agentic model workflows
• Design and run experiments across supervised fine-tuning, reinforcement learning, tool use, evaluation, and model behavior optimization
• Build and maintain distributed RL and post-training infrastructure
• Improve reliability of rollout, evaluation, and reward pipelines
• Own critical parts of the model training stack, including performance, reliability, observability, and debugging
• Investigate and resolve issues across the full stack, from training dynamics and evaluation infrastructure to distributed systems and hardware bottlenecks
• Profile and eliminate performance bottlenecks across GPU, networking, and storage layers
• Build clean abstractions for experiments, model evaluation, and distributed training workflows
• Improve training efficiency, stability, and throughput
• Work closely with founders and domain experts to translate biological problems into model tasks, environments, and evaluation frameworks
• Help turn research improvements into real product and customer advantage

Requirements

• Have hands-on experience training or materially improving serious LLM or generative ML systems
• Have strong software engineering and distributed systems fundamentals
• Have deep experience with Python and modern ML frameworks such as PyTorch, JAX, or equivalent systems
• Have experience with reinforcement learning or post-training methods
• Have built evaluation systems for tool-using or open-ended models
• Have a deep understanding of GPU execution constraints and memory trade-offs
• Have experience debugging performance issues in production ML systems
• Can reason about system-level trade-offs between latency, throughput, and cost
• Have a track record of owning critical production infrastructure
• Can balance research exploration with engineering implementation
• Have experience with distributed systems, large-scale training, or performance-sensitive ML workloads
• Care about code quality, testing, performance, and maintainability

Qualifications

• Communicate clearly and collaborate well under both normal and high-pressure conditions
• Want broad ownership rather than a narrow role boundary

Skills

• Distributed Systems
• Machine Learning
• Reinforcement Learning (RL)

Benefits

• Competitive salary range: $150K - $250K
• Stock options
• Flexible working hours
• Professional development opportunities

Pay

• $150K - $250K

Schedule

• Full-time