Senior / Staff AI Research Engineer, Data Infrastructure

RoboForce; Milpitas (CA); On-site; USD 160,000 - 230,000; Full time

Benefits offered by this job

Competitive stock options/equity

Health, dental, and vision insurance

Visa sponsorship and green card

Job summary

RoboForce in Milpitas, CA is seeking a Senior/Staff AI Research Engineer, Data Infrastructure to own the end-to-end data and learning engine behind the Physical AI stack. You will design and maintain data collection pipelines, annotation workflows, and storage across cloud and on-prem environments.

The role requires 5+ years in CS/Robotics, expert Python, and experience with large-scale datasets, post-training pipelines, and ML workflows (SFT, RL).

Qualifications

• 5+ years of experience in data infrastructure for ML
• Strong Python and production-grade data pipelines
• Experience with large-scale datasets and distributed storage
• Familiarity with post-training infrastructure (SFT, RL) and ML workflows

Responsibilities

• Design and maintain end-to-end data collection pipelines ingesting multimodal demonstration data at scale
• Build annotation tooling and data curation workflows for high-quality training data
• Develop post-SFT reinforcement learning infrastructure and reward scoring
• Build evaluation and test infrastructure to log policy rollouts and surface actionable diagnostics
• Collaborate with ML researchers to define data schemas and pipeline interfaces

Skills

Python

Data pipelines

ETL

Distributed storage

Cloud storage

PyTorch

JAX

Data schemas

Collaboration

Versioning

Education

Bachelor’s or Master’s in Computer Science, Robotics, or related field

Tools

S3

GCS

HDF5

WebDataset

Zarr

MLflow

Weights & Biases

Job description

Why RoboForce

RoboForce is an AI robotics company developing Physical AI-powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

We are looking for a Senior / Staff AI Research Engineer, Data Infrastructure to build the data and learning engine behind RoboForce’s Physical AI stack. In this role, you will own the full pipeline — from raw teleoperation and UMI device data collection through curation, annotation, and storage, to post-training infrastructure that scores demonstrations, identifies failure patterns, and closes the loop back into model retraining.

Responsibilities

Design and maintain end-to-end data collection pipelines ingesting multimodal demonstration data from teleoperation devices and UMI hardware, including synchronization, versioning, and distributed storage at scale.

Build annotation tooling and data curation workflows - quality filtering, deduplication, episode scoring, and domain reweighting - to produce high-quality training datasets for robot policy learning.

Develop post-SFT reinforcement learning infrastructure: implement reward scoring on demonstrations, mine and categorize failure patterns, and feed curated failure data back into the retraining loop.

Build evaluation and test infrastructure to log policy rollouts on-robot, capture structured results, and surface actionable diagnostics for the research team.

Collaborate with ML researchers to define data schemas, episode formats, and pipeline interfaces that support rapid iteration on VLA and manipulation policy training.

Architect scalable storage and retrieval systems for heterogeneous robot data (vision, proprioception, action, language) across both cloud and on-prem environments.

Requirements

Bachelor’s or Master’s degree in Computer Science, Robotics, or related field with 5+ years of experience.

Strong proficiency in Python and experience building production-grade data pipelines and ETL systems.

Hands-on experience with large-scale dataset management, including versioning, deduplication, quality filtering, and distributed storage (e.g., S3, GCS, HDF5, WebDataset, Zarr).

Experience building or working with post-training infrastructure — SFT pipelines, reward modeling, or RL training loops (e.g., PPO, DPO, rejection sampling).

Familiarity with deep learning frameworks (PyTorch, JAX) and ML training workflows sufficient to collaborate tightly with research teams.

Requires 5 days/week in‑office collaboration with the teams.

Bonus Qualifications

Experience with robotics data collection hardware — teleoperation devices, UMI, GELLO, or similar — and the synchronization and preprocessing challenges they introduce.

Familiarity with robot learning pipelines: imitation learning, behavior cloning, or VLA/VLM fine-tuning workflows.

Experience building evaluation or experiment tracking infrastructure (e.g., Weights & Biases, MLflow, custom rollout loggers).

Proven ability to design annotation tooling or human-in-the-loop labeling systems for structured or multimodal data.

Benefits

Competitive stock options/equity programs.

Health, dental, and vision insurance, 401(k) plan.

Visa sponsorship and green card support for qualified candidates.

Lunches and dinners, a fully stocked kitchen, and regular team-building events.