Our Mission

At Laelaps AI, we believe robotics is entering a transformative decade, much like the arrival of the internet. Advances in AI, cloud computing, and hardware are reshaping what autonomous systems can do. Our mission is to build the intelligent software that powers physical security in the real world - enabling robots and sensors to handle dangerous and critical tasks that humans shouldn't have to. By engineering the orchestration layer for intelligent security, we aim to create a world that is safer, more secure, and more resilient.

We're a strong founding team based in Zurich, backed by visionary investors and advisors. We are engineering the future of security today!

The Role

As a Foundational Models Engineer, you'll build the models that power our autonomous security platforms. You'll train, fine-tune, and deploy VLAs, VLMs, and world models that give our robots contextual understanding and intelligent control, pushing the frontier of how machines perceive, reason, and act in the physical world.

This is a hands-on research-engineering role. You won't be writing papers without products. You'll be shipping models that run on real robots in real environments, with all the messy data, hard constraints, and latency budgets that come with embodied AI. You'll work closely with the autonomy and platform teams to close the loop from data collection to model training to fielded deployment.

What You'll Work On

• Train and fine-tune VLA/VLM models for robot control, perception, and contextual reasoning.
• Build data pipelines that turn field-collected video, telemetry, and language signals into training-ready datasets.
• Run experiments at the intersection of foundation models and embodied AI: behavior cloning, RLHF, instruction following, world modeling.
• Optimize models for edge deployment: quantization, distillation, latency tuning for robot-grade compute.
• Close the loop with autonomy: define clean interfaces between learned components and classical autonomy modules.
• Drive a continuous evaluation harness, from sim benchmarks to field metrics on real deployments.

Who We're Looking For

We're looking for a strong ML engineer or research engineer who has shipped foundation model work into production, ideally in an embodied or multi-modal setting. You think rigorously about data, model design, and evaluation. You're comfortable training large models, but you also care deeply about whether they actually work when deployed under real-world constraints.

Your Background:

• PhD or equivalent research experience in ML, computer vision, robotics, or related field.
• Strong PyTorch (or JAX) skills. Comfortable scaling training across multi-GPU and multi-node setups.
• Familiarity with the current VLA/VLM landscape (π0.7, GR00T, and similar) and the underlying research literature.
• Experience taking models from research to deployment: optimization, quantization, latency tuning.