AA
Robotics Research Engineer
Abaka AI
🇺🇸 United States
On-site
2 weeks ago
$100,000 – $140,000
- AI
- Machine Learning
- Computer Vision
- calibration
- Language Models
- Equity
2 weeks ago
- Train and evaluate robot policies (imitation learning, diffusion policies, VLA, RL fine-tuning) on our data and public baselines, in simulation and on real robots.
- Turn evaluation results into a quality signal for the data: which data helped, which did not, and why.
- Track the state of the art: reproduce the papers that matter, run current methods on our hardware, and develop findings that hold up into publications at top robotics, vision, or ML venues.
- Maintain the training and evaluation infrastructure: training runs, evaluation harnesses, and the simulation or world-model environments used ahead of hardware tests.
- Design pipelines that turn raw capture (egocentric human video, robot logs, teleoperation, 3D scans) into training data with minimal human labeling: synthesize what was not captured, auto-label with models and geometry, and curate what goes into training.
- Track which data decisions changed policy performance.
- MS or PhD in Robotics, Computer Science, Machine Learning, or a related field.
- 1 - 3 years of hands-on experience with real robot hardware, through research, an internship, or a project.
- Strong machine learning fundamentals: optimization, generalization, evaluation methodology, and end-to-end model training.
- Strong robotics fundamentals: coordinate frames, kinematics, basic control.
- Strong 3D vision fundamentals: camera models, multi-view geometry, point clouds.
- Rigor in evaluation: claims about a policy or a dataset are backed by measurements.
- Publications or workshop papers at top-tier robotics (CoRL, RSS, ICRA, IROS), vision (CVPR, ICCV, ECCV, 3DV), or ML (NeurIPS, ICML, ICLR) venues. A widely used open-source repository or a dataset adopted by other teams is valued equally.
- Experience with imitation learning or VLA codebases such as LeRobot, ACT, Diffusion Policy, OpenVLA, or π0.
- Experience deploying a learned policy on a real robot and running a rigorous evaluation.
- Practical 3D reconstruction experience: COLMAP, SLAM, 3DGS/NeRF, point-cloud registration.
- World models or video generative models, particularly as simulators or data generators.
- RL fine-tuning of learned policies, or model-based RL.
- Hand and object pose estimation, hand-object interaction, or human-to-robot motion retargeting.
- Depth in a simulator (MuJoCo, Isaac Sim/Lab, Genesis, SAPIEN), including environment and asset authoring.
- Domain randomization, procedural scene generation, or sim-to-real transfer.
- Teleoperation or data collection systems in the ALOHA / UMI / hand-tracking lineage.
- Familiarity with egocentric datasets such as Ego-Exo4D, EgoMimic, HOT3D, Project Aria, and DexCap.
- Dexterous or bimanual manipulation research.
- Camera calibration and multi-sensor time synchronization.
- Vision-language models applied to robotics: task planning, language-conditioned policies, reward or success detection.
- Auto-labeling or ground-truth generation over real sensor data, with quality metrics attached.
- Tactile, force-torque, or other multimodal sensing for manipulation.
- Data curation and dataset-scaling studies: deduplication, mixture ratios, difficulty or diversity scoring, and ablations that identify which data mattered.
Robotics Research Engineer · Abaka AI