ML Researcher Model Adaption / Inference Optimization

Salary: CHF 140'000 - 180'000 per year

Requirements:

• Strong first-principles reasoning and deep technical expertise (e.g. ML systems, compilers, mathematics, physics, competitive programming, security, or HPC)
• Hands-on experience with GPU-level performance engineering (Triton/CUDA) and/or model training and post-training systems
• Creative, high-agency builder who has mastered something hard, with a track record of tackling genuinely novel technical problems
• Comfortable moving fluidly between open-ended research and shipping production code

Responsibilities:

• Design and optimize inference systems that serve continuously adapting models with frontier-level performance and economics
• Develop hardware-informed quantization approaches and write/optimize Triton and CUDA kernels
• Convert agent traces into synthetic reinforcement learning environments for continued model training
• Identify and resolve performance bottlenecks across compute, memory, and communication
• Efficiently deploy LoRAs, learned memory, specialized weights, and other adaptation techniques in production
• Implement policy updates derived from live model interactions and post-deployment feedback loops

Technologies:

• AI
• CUDA
• Hardware
• Model Training
• Security
• PyTorch

More:

A well-resourced AI research and infrastructure team building models that keep learning after deployment, along with the inference systems that serve them at frontier-level speed and cost. Small, technically deep team; distributed by default with regular in-person collaboration.

last updated 36 week of 2026