ML Infrastructure Engineer

About the role

Sunday Robotics builds personal robots for home use and end-to-end ML models for robot manipulation. This role spans data pipelines, training infrastructure, or inference across the full robot learning pipeline.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

General software fundamentals power reliable infrastructure, and you will strengthen them to support agile development.

Distributed training workloads run smoothly across GPU clusters, with minimal friction for researchers as model development accelerates.

High-throughput pipelines ingest, validate, and transform multimodal robot data such as video, proprioception, and actions at scale.

Dataloaders, sharding, and prefetching are optimized to shorten the interval from data arrival to model training for timely iteration.

Requirements

The posting states a bachelor's degree requirement. Strong software engineering and systems fundamentals are required to build and maintain robust infrastructure.

Experience building distributed systems or large-scale data pipelines is necessary for data-intensive robotics workloads.

Hands-on experience with ML training infrastructure, ideally PyTorch, is needed to develop and optimize models.

Comfort reasoning about performance, memory, I/O, and GPU utilization is essential for efficient system design.

Experience managing training workloads with systems such as SLURM, Kubernetes, or similar is required for scalable operations.

An ownership mindset is required to design, build, operate, and iterate on systems end-to-end with reliability.

A degree is stated as a baseline qualification for the role.

Practical notes

The role is based in Redwood City, California, with in-office expectations as needed.

This position requires U.S. work authorization and may require security clearance depending on team assignments.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Roles in ML infrastructure focus on the systems that enable model training and inference at scale.

Common tools include distributed training frameworks, profiling tools, and containerized deployment environments.

Multimodal data pipelines handle video, sensor streams, and control signals for robotics applications.

Real-time inference optimization uses model compression and compilation to meet strict latency targets.

Collaboration with researchers is central to aligning infrastructure with evolving experiment priorities.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.