Member of Technical Staff, Infrastructure

Recruiting From Scratch•San Francisco, CA

1d•Onsite

About The Position

Our client is building general-purpose autonomous robots designed to automate physical labor in real-world industrial environments. The company is developing the infrastructure required to train, deploy, and continuously improve AI models powering a fleet of physically deployed robots. The company has raised approximately $23M from leading investors and has assembled a highly technical team with backgrounds across leading AI, robotics, infrastructure, and technology companies. As a Member of Technical Staff, Infrastructure, you'll work on foundational systems spanning distributed computing, large-scale data pipelines, GPU infrastructure, model training, networking, deployment, and real-time robot operations. This is an opportunity for a high-slope systems engineer who wants broad ownership in an early-stage environment where infrastructure decisions have immediate impact and engineering standards are still being established.

Requirements

2+ years of experience in infrastructure, distributed systems, or related engineering roles
Strong systems engineering background
Experience building production infrastructure or distributed systems
Experience working with large-scale data, compute, or infrastructure systems
Experience at a highly technical, tier-1 VC-backed startup, technology company, or research lab
Experience operating in environments with a high engineering talent bar
Experience owning technical projects end-to-end
Experience working in fast-moving startup or research environments
Strong ability to operate independently with minimal structure
Strong engineering judgment and problem-solving ability
Ability to move quickly between different technical problem areas
Strong communication skills and technical curiosity
Comfortable working on-site in San Francisco 6 days/week
Comfortable working long hours in a highly demanding startup environment
Strong distributed systems fundamentals
Strong systems engineering fundamentals
Experience building scalable infrastructure
Experience with large-scale data pipelines
Experience with cloud infrastructure and distributed compute
Experience with fault-tolerant distributed systems
Experience with networking and infrastructure systems
Experience with large-scale data processing
Strong Python programming skills
Experience working with production infrastructure
Strong understanding of system design and architecture
Ability to troubleshoot complex distributed systems
Ability to reason about performance, reliability, and scalability
Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or related technical field preferred
Strong computer science and systems fundamentals
Equivalent practical engineering experience accepted
Exceptional ownership and execution ability
High technical slope and learning velocity
Strong engineering intuition
Highly hands-on engineering mindset
Comfortable operating with significant autonomy
Strong problem-solving ability
Strong technical communication skills
High energy and urgency
Comfortable working in highly demanding startup environments
Comfortable working across different technical domains
Strong builder mentality
Comfortable operating without established processes
Strong attention to engineering quality
Willing to tackle whichever technical bottleneck is most important
Comfortable making decisions with incomplete information
Strong collaboration skills
Comfortable working closely with highly technical engineers and researchers
Low-ego technical working style
Strong interest in AI, robotics, infrastructure, and distributed systems
Comfortable working 6 days/week onsite in San Francisco

Nice To Haves

Experience with GPU or high-performance computing infrastructure preferred
Experience with ML infrastructure or model training systems preferred
Experience with compute scheduling or orchestration preferred
Strong C/C++ programming skills or experience preferred
Experience with large-scale training infrastructure preferred
Experience with petabyte-scale data systems preferred
Experience with multi-node training orchestration preferred
Experience with real-time or low-latency systems preferred
Experience with robotics or autonomous systems preferred

Responsibilities

Build and own distributed systems infrastructure powering a fleet of deployed autonomous robots
Design infrastructure for large-scale model training and inference workloads
Build training orchestration systems for large-scale ML workloads
Design compute scheduling and resource allocation systems
Build fault-tolerant infrastructure for production AI and robotics workloads
Develop networking infrastructure supporting physically deployed robots
Design and maintain large-scale data pipelines for robot telemetry and video data
Build systems capable of ingesting and processing petabyte-scale datasets
Ensure training infrastructure can continuously ingest and process production robot data
Architect continuous-learning infrastructure connecting deployed robots to model training systems
Build reliable pipelines that move production trajectories from robots back into training
Optimize data infrastructure to keep GPU workloads continuously supplied with data
Build and improve GPU cluster orchestration and compute infrastructure
Work on low-latency infrastructure supporting robot operations across long distances
Design systems capable of meeting demanding latency and reliability requirements
Build internal infrastructure and developer tooling that improves engineering velocity
Identify infrastructure bottlenecks and own solutions end-to-end
Work across different areas of the stack depending on the highest-leverage technical problem
Develop core libraries and infrastructure components used across the engineering organization
Improve observability, reliability, and operational tooling across distributed systems
Establish engineering standards and best practices across infrastructure
Work closely with ML researchers, robotics engineers, and software engineers
Support infrastructure powering large-scale model training and experimentation
Build systems that can reliably operate in real-world production environments
Make architectural decisions in a rapidly evolving technical environment
Operate with significant autonomy and ownership
Move quickly from identifying a bottleneck to designing and shipping a solution
Help shape the company's long-term infrastructure and distributed systems architecture