Member of Technical Staff, ML Infrastructure

Member of Technical Staff, ML Infrastructure

Deep Reach is building the next-generation data infrastructure for robotics. We help bridge the gap between promising robot models and real-world deployment by building the systems, data pipelines, and learning loops needed to make robots improve in production.

We believe robotics progress will be driven not just by better models, but by better data engines: how data is collected, filtered, evaluated, and turned into measurable gains on real tasks. Our team works across robot deployment, teleoperation, data generation, model training, and evaluation, with a strong bias toward hands‑on execution and fast iteration.

As a Member of Technical Staff, ML Infrastructure
, you will build the core systems that make large-scale robotics data and model development possible. You will work on the infrastructure layer behind data ingestion, curation, training, evaluation, experiment tracking, and deployment workflows.

This role is ideal for someone who thinks like a startup engineer but has strong technical curiosity and research awareness. You should be comfortable reading papers or open-source systems, reproducing key ideas, and then building pragmatic internal platforms that help the team move faster and learn faster.

• Build and maintain the infrastructure for robotics data processing, model training, evaluation, and experiment management
• Develop scalable pipelines for ingesting, filtering, curating, versioning, and serving robotics datasets
• Improve internal tooling for training runs, distributed jobs, checkpointing, dataset management, and metrics tracking
• Build systems that connect deployment data, teleoperation data, and model evaluation into a fast iteration loop
• Work closely with research and deployment teammates to remove bottlenecks in training and evaluation workflows
• Design internal benchmarks and experiment infrastructure that make model progress measurable and reproducible
• Read and adapt ideas from research and open-source tooling to improve our internal platform

• Bachelor’s degree or equivalent practical experience in Computer Science, Machine Learning, Systems, or a related field
• Strong engineering experience in ML infrastructure, data platforms, training systems, or large‑scale experimentation frameworks
• Strong coding skills in Python and solid familiarity with modern ML tooling
• Experience with one or more of the following: distributed training, data pipelines, orchestration, experiment tracking, model evaluation systems, dataset versioning, or GPU job infrastructure
• Ability to build fast, pragmatic systems in a startup environment
• Strong ownership and debugging skills across infrastructure and ML workflows
• Interest in robotics, embodied AI, or multimodal learning systems

• You have built internal tooling that significantly accelerated research or ML iteration speed
• You have worked on large-scale data or training systems used by multiple teams
• You are able to understand model development pain points and design infrastructure that solves them
• You can go from ambiguous requirements to reliable internal platforms with minimal supervision
• You care about both engineering quality and iteration speed

• Build the infrastructure layer behind next-generation robotics data and learning systems
• Work at the center of data, models, and deployment feedback loops
• Own foundational systems that shape how the entire team operates
• Join an onsite team that values speed, ownership, and technical range
• Benefits include health insurance, free food, 401(k), and generous PTO

Bay Area, CA (Onsite)