Founding ML Engineer

Own the loop from raw robot experience to a model running on real hardware.

Munari observes what robots see, sense, decide, and do. We want to use that data to detect abnormal behavior, understand failures, evaluate policies, identify recurring failure modes, and help robotics teams improve the next version of their system.

This role sits deliberately between research engineering and production machine learning. You might train a multimodal model, redesign the dataset, optimize its inference path, deploy it to an edge device, and then test it against a real robot in the same week.

We are not looking for research that ends at a chart. We are looking for research that becomes a working system.

Train multimodal models over video, language, proprioception, motor currents, force, tactile, IMU, telemetry, trajectories, and policy traces.

Build models for anomaly detection, episode scoring, failure classification, incident understanding, representation learning, and behavioral evaluation.

Integrate, evaluate, adapt, or fine-tune vision-language-action models and other robot policies across different embodiments.

Develop training and evaluation methods for limited, noisy, heterogeneous, and highly imbalanced real-world robot data.

Turn failures, operator takeovers, recoveries, and successful episodes into evals, labeled datasets, and training signals.

Distill, quantize, prune, compile, and optimize models for deployment on constrained edge hardware.

Design benchmarks, ablations, fault-injection experiments, and metrics that tell us whether a model will actually improve real-world operation.

Work closely with the infrastructure team on distributed training and data systems, and with the edge team on low-latency inference.

Validate ideas in simulation and then close the loop on physical robots.