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Robotics Research Engineer

Abaka AI
CAN
Junior · 1–3 years experience
USD 100000-140000 / year
Discovered 1 months ago

Full Job Posting

  • 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.

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