Jobbeskrivelse
Om jobbet
Stillingsbeskrivelsen fra Hive Robots ApS
About the role
Robots that restock shelves autonomously, at a reliability level a customer signs off on — that's the mission of this role. You will lead our autonomous manipulation effort end to end: the policy stack, the data behind it, the evaluation discipline that keeps the numbers honest, and the architectural direction of the whole track. You'll work with experienced VLA engineers who are ready to go deeper with the right technical lead.
What you'll do
Own the manipulation policy stack from data collection through training to on-robot evaluation
Track frontier AI manipulation research, test it on our hardware, and decide what to adopt, fine-tune, or build
Own the measured success rate — and the methodology that makes it trustworthy
Train models that are robust to sensor noise, partial observability, contact dynamics and environment variability
Collaborate with the rest of the tech stack teams to drive the development of our humanoid software
Ensure grasp quality and contact behaviour from the manipulation side
Mentor and technically lead the engineers on the manipulation track
What we look for
Some combination of the following:
You have trained and deployed a successful manipulation policy on real hardware
Strong real-robot evaluation discipline — you understand how and why benchmark performance differs from field performance, and how to close that gap
A data-first debugging mindset: when a policy underperforms, you look at the data before the model
The seniority to extend and improve an existing codebase, and to raise the technical bar around you
Hands-on experience developing and deploying robot learning algorithms on hardware
A genuine drive towards reliability in the field
Tech stack
Python, PyTorch, VLA (vision-language-action) model families, RL
On-robot inference deployment (ONNX/TensorRT-class)
Data logging and real-robot evaluation pipelines
Git, GitHub, Docker and CI for reproducible training and deployment
Training infrastructure for imitation and policy learning at scale
Expertise working in Linux
Bonus experience
Humanoid or bimanual manipulation
Retail or logistics industry
Perception knowledge: object pose estimation, depth estimation models, success detection
Grasping based on touch sensors
Simulation tools like NVIDIA Isaac Sim or MuJoCo
Kilde: Originalt opslag på Jobnet ↗ · Opslags-ID: 855182ad-17c1-4e03-b6db-bcc7b386007a