Senior Machine Learning Engineer

ppbHexagon Robotics /b is a division of Hexagon – a global leader in precision measurement. The division develops humanoid robots for industrial sectors to address labor shortages and accelerate the transition from automation to autonomy. Our first humanoid, AEON, was launched in June 2025 and is already in pilots with five customers. /p pWe are hiring a bSenior Machine Learning Engineer /b to join our Foundation Model team, focused on building, evaluating, and deploying state‑of‑the‑art foundation models for humanoid robotic manipulation. You will work on making modern models (VLA, world action models, diffusion policies) work reliably and efficiently on a real industrial humanoid robot. /p h3Your Mission /h3 ul liEvaluate and benchmark foundation models (e.g. VLA, world action models, diffusion-based policies) on real robotic manipulation tasks /li liDesign and run experiments to understand model behavior, data requirements, failure modes, and generalization limits on humanoid hardware /li liDevelop fine-tuning and adaptation methodologies to specialize large pre-trained models for industrial‑grade performance on targeted tasks /li liBuild and maintain clean evaluation pipelines, benchmarks, and metrics to support rigorous model comparison and selection /li liWork closely with MLOps/DataOps and Learning teams to integrate selected models into our end‑to‑end learning and deployment pipeline /li liCollaborate with Robotics and Product teams to ensure model choices align with real‑world constraints and customer workflows /li /ul h3Your Skillset /h3 ul liPhD or MSc in Machine Learning, Robotics, Computer Science, or a related field /li liSolid understanding of modern multimodal model architectures and training methods /li liStrong hands‑on experience with modern deep learning frameworks (e.g. pytorch/jax) /li liExperience designing experiments and making data‑driven technical decisions /li /ul pStrong advantage if you have experience with the following; /p ul liExperience with VLAs, world action models, diffusion policies, etc. for robotic manipulation /li liFamiliarity with efficient fine‑tuning techniques (e.g. LoRA, adapters, distillation) /li liExperience working with real‑world, imperfect datasets and hardware‑in‑the-loop evaluation /li liPublications, open‑source contributions, or strong applied research projects in ML and/or robotics /li /ul h3What You’ll Get /h3 ul liFlexible working hours and a hybrid model for real work‑life balance /li liCHF 500 mobility credit for sustainable commuting /li liBonus system strong pension contributions /li liTailored training development opportunities /li liRelocation support for a smooth start /liliDiscounts on health, mobility entertainment /li liTeam events and a flat hierarchy where your voice counts /li liA warm, international culture built on respect and collaboration /li /ul /p #J-18808-Ljbffr