Research Scientist

Noumenal Labs is a deep tech AI company focusing on outdoor robotics. The Research Scientist role involves designing and implementing benchmarking frameworks for uncertainty-aware autonomy, with a focus on active inference in teleoperation-augmented robotics platforms.

Responsibilities:

• Co-design and implement benchmarking protocols comparing active inference agents to:
• Conventional reinforcement learning (RL) baselines
• RL systems augmented with uncertainty estimation
• Evaluate performance across:
• Data efficiency
• Safety under distribution shift
• Directed exploration
• Sim-to-real robustness
• Teleoperation scaling efficiency
• Explainability
• Integrate benchmarking into a standardized teleoperation control protocol where agents decide when to:
• Continue autonomous execution
• Request human takeover under a constrained intervention budget
• Develop metrics capturing:
• Human scalability (operator-to-robot ratio, intervention allocation efficiency)
• Safety under uncertainty (timeliness and selectivity of handovers)
• Autonomous work efficiency (task completion under limited supervision)
• Align benchmarking workloads with the broader teleoperation platform architecture:
• On-robot control and safety systems
• Near-edge inference (uncertainty estimation, planning, intervention logic)
• Cloud-based training, analytics, and fleet management
• Ensure benchmarks reflect real system constraints:
• Latency budgets
• Network degradation and connectivity loss
• Multi-robot resource sharing
• Execute experiments across a staged pipeline:
• Tier 1: Controlled simulation (e.g., MuJoCo environments)
• Tier 2: High-fidelity robotic simulation (e.g., RLBench, ManiSkill)
• Tier 3: Real-world or dataset-driven validation
• Maintain consistency via a shared teleoperation surrogate (expert policy / planner) to emulate human intervention
• Quantify and analyze:
• Calibration of uncertainty signals
• Intervention precision/recall
• Learning from intervention (post-handover improvement)
• Stability across repeated autonomy–human control cycles
• Compare whether:
• Native probabilistic approaches (active inference)
• Retrofitted uncertainty (ensembles, Bayesian heads, etc.)
• Heuristic baselines
• Best optimize teleoperation efficiency
• Profile compute and system behavior of active inference workloads within the teleoperation stack:
• World model rollouts
• Posterior inference
• Intervention decision logic
• Contribute to:
• Near-edge workload allocation strategies
• Fleet scaling models (robots per server)
• Latency vs. safety tradeoffs
• Reproducible benchmarking suite and datasets
• Technical reports and whitepapers
• Conference publications (robotics / ML / systems venues)
• Design recommendations for teleoperation and autonomy stacks
• Cross-team guidance for infrastructure, controls, and ML teams