Staff ML Systems Engineer, Distributed Systems
Seattle, WA
- Pay
USD 195,000–230,000/year — pay source
Salary 195,000 – 230,000 USD per year Our salary range is generous and we consider each individual’s background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
Read the full posting- Work setting
On-site — work setting source
Work setup in the employer’s listing: onsite
From the employer’s posting- Employment
Full-time — employment source
Employment type Full time
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and build scalable distributed ML pipelines for processing, training, evaluation and post-processing
Architect distributed execution, including parallelism, scheduling, resource allocation and fault tolerance
Build reusable abstractions and libraries that simplify distributed pipeline development
Optimize throughput, utilization and reliability across distributed CPU and GPU environments
Improve observability, debugging and monitoring for distributed systems at scale
From the employer’s posting
Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows.
Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms.
Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development.
Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability.
Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
Tools in this posting
- Python
- Kubernetes
- Spark
- Dask
- PyTorch
- TensorFlow
Source — Tool mentions in context
- 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems. - Strong Python programming skills, including experience with concurrency, performance optimization, and systems development. - Experience with distributed computing frameworks such as Ray, Spark, Dask, Flink, or similar technologies.
- Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies. - Experience operating Kubernetes-based infrastructure and large-scale cloud systems. - Deep understanding of distributed systems concepts including data locality, serialization costs, scheduling, and resource management.
- Strong Python programming skills, including experience with 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.
- Experience building infrastructure for machine learning training and inference systems. - Familiarity with modern ML frameworks such as PyTorch or TensorFlow. - Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies.
Posting record
Report an errorOpen 4 monthsWe first saw this listing 147 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- May 29, 2026 · 147 days ago
- Recorded sightings
- 97
- Last seen by us
- Oct 24, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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- Have your resume and examples of relevant work ready.
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Job description
We are seeking a Staff ML Systems Engineer to architect and build the distributed infrastructure that powers large-scale machine learning workflows across the organization.
This role sits at the intersection of machine learning, distributed systems, and platform engineering. You will be responsible for designing scalable systems that support data processing, model training, evaluation, and post-processing pipelines while enabling ML teams to efficiently develop, operate, and scale production-grade workflows.
You will play a critical role in defining the architectural patterns, tooling, and infrastructure that underpin our machine learning platform.
What You'll Get To Do
- Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows.
- Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms.
- Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development.
- Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability.
- Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency.
- Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development.
- Establish best practices and engineering standards for distributed machine learning infrastructure.
- Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs.
- Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
What You Have
- 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems.
- Strong Python programming skills, including experience with 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 skills with demonstrated expertise in scalability, reliability, and performance optimization.
- Experience diagnosing and resolving bottlenecks in distributed environments.
- Ability to work cross-functionally and drive technical decisions across multiple teams.
The Extras That Set You Apart
- Experience building infrastructure for machine learning training and inference systems.
- Familiarity with modern ML frameworks such as PyTorch or TensorFlow.
- Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies.
- Experience operating Kubernetes-based infrastructure and large-scale cloud systems.
- Deep understanding of distributed systems concepts including data locality, serialization costs, scheduling, and resource management.
- Experience with distributed debugging, observability, and workflow orchestration platforms.
- Proven ability to establish technical direction and influence architecture across organizations.
Our salary range is highly competitive with the market, but we take into consideration an individual's background and experience in determining final salary. Base pay offered may vary depending on geographic location, job-related knowledge, skills, and experience.
In addition to competitive compensation, FieldAI offers comprehensive benefits, equity participation, and the opportunity to contribute to cutting-edge advancements in AI and robotics.
Salary
195,000 – 230,000 USD per year
Employment type
Full time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Salary 195,000 – 230,000 USD per year Our salary range is generous and we consider each individual’s background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
- Location & working pattern
Seattle, WA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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