Member of Technical Staff, Model Training (infra & platform)

• Architect, build, and scale shared infrastructure for supervised fine-tuning (SFT), reinforcement learning (RL), and on-policy distillation (OPD), supporting language, multimodal, and robotics workloads.
• Design distributed training and execution pipelines that coordinate GPU workers, rollout generation, teacher-model inference, reward computation, and simulation environments.
• Develop high-performance infrastructure for model training and inference, optimizing GPU utilization, memory management, communication, storage, and distributed execution.
• Build and evolve the core architecture of the hiring partner's agent ecosystem, including execution runtimes, tool interfaces, environment integrations, model registries, and serving APIs.
• Develop robust systems for experiment tracking, dataset and model versioning, checkpointing, fault recovery, and observability to improve research reproducibility and operational reliability.
• Identify and eliminate infrastructure bottlenecks, reducing experiment turnaround time and enabling researchers to run increasingly complex training workloads.
• Define technical standards, platform interfaces, and architectural patterns that support collaboration across research, infrastructure, and applied engineering teams.
• Lead critical engineering initiatives from system design through implementation and production operations, mentoring engineers while maintaining direct ownership of technically challenging components.

• Strong computer science fundamentals, particularly in distributed systems, concurrency, networking, and performance engineering.
• Extensive experience designing, building, and operating scalable backend systems or distributed computing platforms.
• Proficiency in Python and at least one systems-oriented programming language, such as C++, Rust, or Go.
• Hands-on experience with GPU-based machine learning infrastructure, distributed training, model inference, or large-scale compute orchestration.
• Strong understanding of system performance, including compute utilization, memory efficiency, communication overhead, scheduling, and fault tolerance.
• Experience designing reliable APIs, services, and infrastructure components that support complex engineering or research workflows.
• Demonstrated technical ownership, including making architectural decisions, leading cross-functional initiatives, and delivering systems that other engineers depend on.
• Ability to operate effectively in fast-moving research environments where requirements evolve and infrastructure must support rapid experimentation.

• Experience with distributed ML frameworks and technologies such as PyTorch Distributed, DeepSpeed, Ray, Megatron-LM, or similar systems.
• Familiarity with GPU inference engines and optimization frameworks such as vLLM, TensorRT-LLM, or equivalent technologies.
• Experience building reinforcement learning infrastructure, rollout orchestration systems, or distributed agent execution environments.
• Knowledge of CUDA, GPU kernel optimization, NCCL, or low-level GPU performance engineering.
• Experience with Kubernetes, container orchestration, cluster scheduling, and cloud-based GPU infrastructure.
• Familiarity with simulation platforms, robotics infrastructure, or large-scale agent evaluation systems.
• Contributions to open-source infrastructure, distributed computing frameworks, or high-performance machine learning systems.

RequiredAlso useful

Project and contracting process: the hiring partner. Applications continue on the provider's website.