ML Infrastructure Engineer
$180,000–$350,000 year
Remote
Job Summary
Build robust, flexible, and scalable RL and post-training pipelines, including smoke tuning runs for quality testing and approach ablations. Design data control systems that govern what the model sees, when it sees it, and how training data flows through rollouts, replay, filtering, evaluation, and policy updates. Tune training and inference end-to-end for high throughput across the systems that matter: networking, memory, compute scheduling, data loading, storage, checkpointing, and I/O. Investigate how infrastructure choices affect learning dynamics, eval quality, model behavior, and training stability. Build infrastructure for model iteration: experiment runs, artifacts, evals, dashboards, failure inspection, reproducibility, and cost visibility. Work on inference infrastructure where it affects post-training and evaluation loops. Build and improve agentic development environments: coding-agent harnesses, browser/tool integrations, terminal/runtime sandboxes, repo-aware workflows, and multi-agent orchestration.
Required Qualifications
- Have designed, built, or maintained distributed RL/post-training systems at scale and are fluent in their moving parts: rollouts, replay buffers, reward signals, data filtering, policy updates, evaluation loops, and failure analysis
- Are familiar with deep learning frameworks such as PyTorch or JAX
- Are proficient in Python, including concurrency, asynchronous programming, multiprocessing, and performance optimization
- Can debug distributed GPU workloads across CUDA runtime, container runtime, driver versions, NCCL or equivalent communication layers, networking, storage, scheduling, and checkpointing
- Have experience with profiling tools across the stack, for example py-spy, PyTorch profiler, Nsight, perf, tracing, metrics, logs, or custom instrumentation
- Have experience with inference stacks such as vLLM, SGLang, TensorRT-LLM, Dynamo, or custom serving infrastructure
- Can reason from system metrics back to model behavior: when latency, queueing, sampling, data order, rollout throughput, or infrastructure failures affect learning
- Have a strong ownership mindset: you can take an ambiguous infrastructure problem, make it concrete, ship a working system, and improve it from real feedback
- Please submit your application in English
Desired Qualifications
- A public builder footprint: open-source contributions to RL, distributed ML, LLM training, inference, eval, or agent infrastructure – repos, PRs, benchmarks, papers with code, technical posts – and a good technical X/Twitter presence with live building, debugging threads, and useful interaction with strong builders
- Experience in a high-bar AI infra, research, or model environment such as xAI/Grok, Qwen, ByteDance AI infra/research, Prime Intellect, or similar teams
- Custom training framework support or ownership: distributed training, fine-tuning pipelines, trainers, schedulers, checkpointing, data loaders, model/eval integration, or performance tooling
- Serious use of Claude Code, Codex, Kimi Code, Pi Agent, Droid, or similar agentic coding systems as a development surface
- Experience with GPU clusters on Kubernetes, Slurm, Ray, custom schedulers, or cloud GPU orchestration
- NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE, or EFA
- Rust, C++, CUDA, Go, or systems-level performance work
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