Field AI
Staff ML Systems Engineer, Distributed Systems
$195K–$230K
Seattle, WA +1 · On-site · Staff · Full-time · Posted May 29, 2026
- Location
- Seattle, WA
Irvine, CA - Experience
- 5+ years; no alternative stated.
About the job
Architects and builds distributed infrastructure for large-scale machine learning data, training, evaluation, and post-processing workflows.
What you'll do
- Design and optimize distributed ML pipelines and execution systems, including scheduling, parallelization, resource allocation, and fault tolerance.
- Build reusable frameworks and improve performance, reliability, observability, and debugging across distributed CPU and GPU environments.
- Partner with ML, data, and infrastructure teams to productionize research workflows and establish engineering standards.
What they ask for
- 5+ years building distributed systems, backend infrastructure, ML platforms, or large-scale data processing systems.
- Strong Python programming skills, including concurrency, performance optimization, and systems development.
- Experience with distributed computing frameworks such as Ray, Spark, Dask, Flink, or similar technologies.
- Experience designing and scaling data pipelines or ML workflows; strong system design and distributed bottleneck diagnosis skills.
Nice to have
- Experience with ML training and inference infrastructure, PyTorch or TensorFlow, or multi-node/multi-GPU training technologies such as DDP, FSDP, or DeepSpeed.
- Experience with Kubernetes, large-scale cloud systems, distributed debugging, observability, or workflow orchestration.
Pay
USD 195,000–230,000 per-year-salary; base pay
Tools
Python, Ray, Spark, Dask, Flink, PyTorch, TensorFlow, Kubernetes
AI focus
Builds and operates distributed infrastructure for machine learning workflows, including model training, evaluation, and productionization.
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