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Applied Researcher - Deployment Intelligence

DYNA Robotics· Research
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Redwood City, CAFull-timePosted 3mo ago

About the role

Applied Researcher on the AI Research team focused on mining deployment data to improve fleet performance, building monitoring and data-to-model improvement pipelines, and delivering end-to-end shipped improvements in real-world robot deployments.

Responsibilities

  • Continuous Learning Loops: Design and ship pipelines that turn real deployment data into targeted fine-tuning and online policy improvement, closing the loop from field to model without a full retrain cycle every time.
  • Fleet Data Analytics: Mine high-frequency multimodal sensor and video data across tens of thousands of fleet episodes to catch failure modes, drift, and regressions before they become customer-visible.
  • 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.
  • 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.
  • Cross-Scene Generalization: Characterize and close generalization gaps as robots move to new sites, lighting, layouts, and objects; build evaluation harnesses and data-selection strategies for day-one performance at new sites.
  • End-to-End Ownership: Partner with Research, Data, and Deployment teams to turn a finding into a shipped improvement, from data-analysis notebook to production monitoring dashboard to deployed model update.

Qualifications

  • Bachelor’s, Master’s, or PhD in CS, Robotics, Statistics, or related field, or equivalent practical experience; degree level not strictly required.
  • Applied ML Depth: Hands-on experience in at least two of: reinforcement learning, sensor-data modeling/anomaly detection, vision-language models, or continual/online learning.
  • Production Instincts: Experience building monitoring, evaluation, or data pipelines for a live ML system.
  • Experimentation & Statistics: Comfortable designing and interpreting production experiments (A/B tests, canary rollouts, staged fleet deployments) with statistical rigor.
  • Technical Stack: Strong Python and PyTorch (or JAX); experience with large multimodal datasets and distributed compute (Slurm/GPU clusters).
  • Communication: Ability to translate fleet-scale data investigations into actionable recommendations for researchers and operators.

Skills

Required:PythonPyTorch (or JAX)Reinforcement LearningExperimentation & statisticsCommunication
Nice to have:Sensor-data modeling / anomaly detectionVision-language models
Last updated Sep 23, 2026. Interested? Apply on the company site.

About DYNA Robotics

Commercial-Grade Robots for Real-World Automation

DYNA: The first production-ready foundation model robot — built to reliably deploy in the wild.

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