Forward Deployed, Robotics Engineer

Black Forest Labs Black Forest Labs · Multimodal · Freiburg · GTM

Forward Deployed Robotics Engineer to integrate and deploy Black Forest Labs' generative models (Latent Diffusion, Stable Diffusion, FLUX, action/VLA models) with robotics and physical-AI customers. This role involves embedding with customers, shipping integrations, fine-tuning models, optimizing for latency and quality on real robots (on-prem and edge), prototyping use cases, and collaborating with research on novel techniques. Requires robotics engineering experience, customer interaction, and hands-on experience with action/VLA models and related ecosystems.

What you'd actually do

  1. Partner with customers to design and ship integrations, finetunes, and interfaces on top of BFL's models, including our action / VLA models from first prototype to production.
  2. Get models running _well_ in the customer's environment: strong latency and output quality on real robots, across on-prem and BFL-hosted deployments, including real-time and on-device / edge constraints.
  3. Find the highest-value use cases with customers and prototype solutions fast.
  4. Identify and own opportunities to expand BFL's technologies into new domains
  5. Prototype novel techniques from recent research papers in close collaboration with our research team

Skills

Required

  • robotics engineer
  • forward deployed engineer
  • technical founder
  • customer interaction
  • iterating on solutions
  • tailored support for serving generative AI models
  • action / VLA models
  • imitation learning
  • reinforcement learning
  • policy learning
  • model hosting
  • backend engineering
  • frontend engineering
  • UI development
  • communication skills
  • collaboration with non-technical stakeholders

Nice to have

  • diffusion models
  • flow matching
  • fine tuning
  • distillation techniques
  • inference optimizations for transformer based machine learning models
  • architect solutions in complex enterprise environments
  • open-source contributions (diffusion models, robotics, simulation)
  • cloud platforms
  • state of the art deployment solutions

What the JD emphasized

  • ship real integrations on their robots
  • shipped robot-learning systems that real people use
  • serving generative AI models
  • action / VLA models
  • model hosting and backend engineering
  • inference optimizations

Other signals

  • customer integration
  • robotics
  • generative models
  • physical-AI