micro1
Member of Technical Staff, Model Training (infra & platform)
Remote engineering role at micro1 owning the distributed infrastructure and platform behind model training — building GPU pipelines for SFT/RL/distillation, agent execution runtimes, and observability so researchers can train and deploy AI models faster. Core stack: Python plus a systems language (C++/Rust/Go) and GPU-scale tooling.
- Compensation
- $200K–$320K / yr
- Location
- Not listed
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Strong CS fundamentals 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 language (C++, Rust, or Go)
- Hands-on experience with GPU-based ML infrastructure, distributed training, model inference, or large-scale compute orchestration
- Strong understanding of system performance: compute utilization, memory efficiency, communication overhead, scheduling, fault tolerance
- Experience designing reliable APIs, services, and infrastructure components for engineering or research workflows
- Demonstrated technical ownership: architectural decisions, leading cross-functional initiatives, delivering systems other engineers depend on
- Ability to operate in fast-moving research environments with evolving requirements
Nice to have
- Experience with distributed ML frameworks such as PyTorch Distributed, DeepSpeed, Ray, or Megatron-LM
- Familiarity with GPU inference engines such as vLLM or TensorRT-LLM
- Experience building reinforcement learning infrastructure, rollout orchestration, 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 ML systems
Full description
Job Title: Member of Technical Staff, Model Training (infra & platform)
Job Type: Full-time
Location: Remote
The Role
In this role, you will take technical ownership of the distributed systems and platforms that enable researchers to train increasingly capable models, execute large-scale experiments, and deploy specialized AI systems efficiently.
You will work across distributed training, GPU infrastructure, inference optimization, simulation environments, and agent execution frameworks, making critical architectural decisions that directly influence research velocity and model performance.
What You'll Do
- 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 micro1'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.
What We're Looking For
- 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.