Oakwell Hampton Group
Member of Technical Staff - ML (Palo Alto)
Oakwell Hampton Group, Palo Alto, California, United States, 94306
My client are hiring a Founding Member of the Technical Staff, Machine Learning, to help build frontier AI systems applied to complex engineering and reasoning domains. This role is designed exclusively for candidates coming from top tier AI research labs or equivalent frontier environments.
This is a hands on, 0 to 1 role with ownership across model development, post training, and production deployment. You will work at the cutting edge of large language models, reinforcement learning, and multi agent systems, with direct impact on how these systems are designed, trained, and scaled in real world use.
Key Roles and Responsibilities
Train and post train advanced ML models for reasoning, structured generation, and tool use Own reinforcement learning workflows including reward modeling, environment design, and scaling Design and implement multi agent systems that reason, generate, and verify outputs Build end to end ML pipelines covering data curation, training, evaluation, and deployment Prototype rapidly, benchmark rigorously, and push models toward production readiness Optimize models and systems for performance, latency, and cost under real constraints Collaborate closely with engineers and domain experts to integrate ML into production workflows Influence core architectural decisions across ML systems and infrastructure
Ideal Candidate
Background at a frontier AI research lab or equivalent environment only Proven experience taking ML models from research through real world deployment Deep hands on expertise in reinforcement learning and post training beyond publication work Strong experience training and fine tuning LLMs or code models for reasoning and structured tasks Practical experience with distributed training and serving systems and local model deployment Builder mindset with the ability to move quickly from prototype to production Comfortable operating with high ownership and ambiguity in an early stage environment
This is a hands on, 0 to 1 role with ownership across model development, post training, and production deployment. You will work at the cutting edge of large language models, reinforcement learning, and multi agent systems, with direct impact on how these systems are designed, trained, and scaled in real world use.
Key Roles and Responsibilities
Train and post train advanced ML models for reasoning, structured generation, and tool use Own reinforcement learning workflows including reward modeling, environment design, and scaling Design and implement multi agent systems that reason, generate, and verify outputs Build end to end ML pipelines covering data curation, training, evaluation, and deployment Prototype rapidly, benchmark rigorously, and push models toward production readiness Optimize models and systems for performance, latency, and cost under real constraints Collaborate closely with engineers and domain experts to integrate ML into production workflows Influence core architectural decisions across ML systems and infrastructure
Ideal Candidate
Background at a frontier AI research lab or equivalent environment only Proven experience taking ML models from research through real world deployment Deep hands on expertise in reinforcement learning and post training beyond publication work Strong experience training and fine tuning LLMs or code models for reasoning and structured tasks Practical experience with distributed training and serving systems and local model deployment Builder mindset with the ability to move quickly from prototype to production Comfortable operating with high ownership and ambiguity in an early stage environment