Dyna Robotics

Applied Researcher - Deployment Intelligence & Continuous Learning

Redwood City, CA

Sponsorship not specifiedDetected 17 hours ago

PythonMachine LearningPyTorchData AnalysisData EngineeringStatisticsA/B TestingForecastingRoboticsResearchCommunicationProblem Solving

About the role

Our models don't stop learning at deployment.
A growing fleet of robots is generating real production data every day, and the gap between "works in the lab" and "works at a new customer site, forever" is a research problem, not just an ops one.

Automated Fleet Monitoring: Build automated monitoring that flags anomalies, near-failures, and out-of-distribution scenes across the fleet in real time, and that decides what needs a human versus what the system can self-correct.
build the evaluation harnesses and data-selection strategies that make day-one performance at a new customer site predictable.
End-to-End Ownership: Partner with Research, Data, and Deployment teams to turn a finding into a shipped improvement, from a data-analysis notebook to a production monitoring dashboard to a deployed model update.
We want someone driven by shipped impact and genuine passion for the problem, not research for its own sake.
Production Instincts: Experience building monitoring, evaluation, or data pipelines for a live ML system, comfortable with the ambiguity of real-world fleet data versus curated benchmarks.
Cross-Scene Generalization: Characterize and close generalization gaps as robots move to new sites, lighting, layouts, and objects; build the evaluation harnesses and data-selection strategies that make day-one performance at a new customer site predictable.
Experience building or fine-tuning perception or foundation models for automated monitoring, captioning, or anomaly detection.

Bachelor's, Master's, or PhD in CS, Robotics, Statistics, or a related field, or equivalent practical experience.
Degree level doesn't matter to us
what matters is genuine passion for the work and a track record of hands-on effort that shipped into a real system, not just a benchmark.
Experience with robot fleets or other physically-deployed autonomous systems in the field, not just simulation.

Hands-on experience in at least two of: reinforcement learning, sensor-data modeling/anomaly detection, vision-language models, or continual/online learning.
RL for Deployment: Apply reinforcement learning (offline RL, RL fine-tuning, reward modeling from human and teleop feedback) to improve policies directly from real-world deployment data, not just simulation.
Educational Background: Bachelor's, Master's, or PhD in CS, Robotics, Statistics, or a related field, or equivalent practical experience.
Apply reinforcement learning (offline RL, RL fine-tuning, reward modeling from human and teleop feedback) to improve policies directly from real-world deployment data, not just simulation.

Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance.
Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world.
Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors.

This listing is sourced directly from Dyna Robotics's careers page and normalized into a canonical job model.