Applied Researcher - Deployment Intelligence

What you'll do

• Build pipelines that use deployment data for fine-tuning and online policy improvement.
• Analyze fleet sensor and video data to identify failure modes, drift, and regressions; build real-time fleet monitoring.
• Apply reinforcement learning and develop evaluation and data-selection methods to improve performance across customer sites.

What they ask for

• Hands-on experience in at least two of reinforcement learning, sensor-data modeling/anomaly detection, vision-language models, or continual/online learning.
• Experience building monitoring, evaluation, or data pipelines for a live ML system.
• Strong Python and PyTorch (or JAX); comfortable with large multimodal datasets and distributed compute (Slurm/GPU clusters).
• Experience designing and interpreting production experiments and applying statistical rigor.

Nice to have

• Experience with robot fleets or other physically deployed autonomous systems.
• Experience with perception or foundation models, anomaly and drift detection, or human-in-the-loop learning.

Tools

Python, PyTorch, JAX, Slurm

AI focus

Use deployment data, reinforcement learning, monitoring, and evaluation to improve robot policies and generalization in real-world settings.