Job description

FieldAI ’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. 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 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. Why Join FieldAI in Irvine? In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics’ hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use. You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world. Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like. We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status.

Requirements

Must-haves

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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomlmachine learningdesign systemspythonkubernetes

Platform: lever
Effort: quick