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.

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