Staff ML Systems Engineer, Distributed Systems

Location: Seattle, WA or Irvine, CA; on-site

Company

hirify.global builds risk-aware, reliable, field-ready embodied AI systems for real robots, sensors, and robotics deployments.

What you will do

• Design and build scalable distributed machine learning pipelines for data processing, model training, evaluation, and post-processing.
• Architect distributed execution systems covering parallelization, workload scheduling, resource allocation, and fault tolerance.
• Develop reusable abstractions, frameworks, and libraries for distributed pipeline development.
• Optimize CPU and GPU workloads, including data partitioning, memory utilization, serialization, throughput, and compute efficiency.
• Partner with ML engineers, data engineers, and infrastructure teams to productionize research workflows and support large-scale model development.
• Improve engineering standards, observability, debugging, monitoring, and operational tooling for distributed systems.

Requirements

• 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems.
• Strong Python 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 machine learning workflows.
• Strong system design expertise with a focus on scalability, reliability, and performance optimization.
• Ability to work on-site in Seattle, WA or Irvine, CA.

Nice to have

• Experience building infrastructure for ML training and inference systems.
• Familiarity with PyTorch or TensorFlow.
• Experience with multi-node or multi-GPU training, including DDP, FSDP, or DeepSpeed.
• Experience operating Kubernetes-based infrastructure and large-scale cloud systems.
• Experience with distributed debugging, observability, and workflow orchestration platforms.

Culture & Benefits

• Work on embodied AI systems that are tested on real hardware and improved through field deployments.
• Competitive compensation based on background, geographic location, knowledge, skills, and experience.
• Comprehensive benefits and equity participation.
• Opportunity to contribute to advances in AI and robotics.