Machine Learning Engineer

We are looking for a Machine Learning Engineer to join the team - someone who can operate across the full spectrum from research to production. This role sits closer to the research end: you’ll be pushing the frontier on generative models for physical design while also shipping real systems that engineers use every day. Please show both the quality of your past research and any production impact it has had.

Must Haves

• PhD in Machine Learning, Computer Science, Applied Mathematics, or a closely related field, with original contributions to deep learning, reinforcement learning, or generative models.
• Formal background in generative modelling - working knowledge of the transformer architecture, diffusion models, flow matching, and variational autoencoders: their evolution, their tradeoffs, and where they’re going.
• Real world experience building ML/AI systems that reached production, not just research prototypes.
• Practical experience managing research projects end to end - from problem formulation through to evaluation and deployment.
• Knowledge of modern, larger-scale Python and the ML stack (PyTorch, JAX, or equivalent). You write research-grade code.
• Practical experience building large-scale data pipelines. We don’t have data infrastructure - you’ll help build it.

Nice to Have

• Experience in a high-pace startup environment.
• Knowledgeable about physical engineering or related domains such as robotics or cognitive science.
• Experience working with PINNs (physics-informed neural networks) or graph neural networks for physics-based surrogate models.
• Experience owning or being involved longer-term in an open source project, ideally in a related field such as ML tooling or scientific computing.
• Experience with GPU cluster orchestration.
• Experience with vector embeddings, ideally retrieval-augmented generation (RAG) and multi-modal representations (e.g. CLIP).
• Experience with model fine-tuning.
• Experience with Markov chains or (partially-observable) Markov decision processes.

• PhD in Machine Learning, Computer Science, Applied Mathematics, or a closely related field, with original contributions to deep learning, reinforcement learning, or generative models.
• Formal background in generative modelling - working knowledge of the transformer architecture, diffusion models, flow matching, and variational autoencoders: their evolution, their tradeoffs, and where they’re going.
• Real world experience building ML/AI systems that reached production, not just research prototypes.
• Practical experience managing research projects end to end - from problem formulation through to evaluation and deployment.
• Knowledge of modern, larger-scale Python and the ML stack (PyTorch, JAX, or equivalent). You write research-grade code.
• Practical experience building large-scale data pipelines. We don’t have data infrastructure - you’ll help build it., * Experience in a high-pace startup environment.
• Knowledgeable about physical engineering or related domains such as robotics or cognitive science.
• Experience working with PINNs (physics-informed neural networks) or graph neural networks for physics-based surrogate models.
• Experience owning or being involved longer-term in an open source project, ideally in a related field such as ML tooling or scientific computing.
• Experience with GPU cluster orchestration.
• Experience with vector embeddings, ideally retrieval-augmented generation (RAG) and multi-modal representations (e.g. CLIP).
• Experience with model fine-tuning.
• Experience with Markov chains or (partially-observable) Markov decision processes.