Senior ML Engineer

AI/ML tech start-up, developing a novel foundation model, with a singular vision: to achieve fully automated, unsupervised software delivery in embedded control systems. Based in West London backed by venture capital. Job Description Lead research, development, and deployment of foundation model, define technical strategy, optimize performance, and ensure scalability for AI/ML start-up in London. What you will do: - Lead the research, development, and production deployment of Hypercritical's foundation model. - Define the long-term technical strategy for high-performance Machine Learning systems. - Optimize the model for peak performance across diverse hardware and ensure scalability for exponential user growth. - Serve as the technical guardian for the model's quality and Service Level Objectives (SLOs). - Provide a hands-on solution architecture for the core ML infrastructure. - Select, evaluate, and implement state-of-the-art technologies (e.g., distributed training, specialized hardware, efficient serving frameworks). - Profile and optimize the end-to-end ML stack: data pipelines, training loops, inference serving, and deployment. - Design and implement GPU-accelerated components, including custom CUDA kernels where off-the-shelf libraries are not enough. - Work closely with the founders to translate product requirements into concrete optimization goals and technical roadmaps. - Build internal tooling, benchmarks, and evaluation harnesses that make it easy for the rest of the team to experiment, debug, and ship safely. Why work with us: - Youll make a significant impact, getting in on the ground floor of a company that will alter software development forever. - You will shape how a never-before-seen foundation model is trained, optimized, and deployed. - Our level of transparency is unusual. No management jargon, you will get the simple truth, never a lie. About the Interview: - No live or take-home coding tasks. 2 interviews max, 1hr (cultural, online) with CEO and 2hr (technical, face-to-face) with CPO. Requirements Expertise in designing, architecting, and implementing large-scale foundation models Significant hands-on experience optimising and debugging deep learning models Practical experience with distributed or large-scale training and inference Deep understanding of at least one major deep learning framework (ideally PyTorch) Experience building and operating ML systems on cloud platforms Passion and determination Able to grind through complicated and ambiguous problems Delivery-oriented; respects timelines and commitments Openness to disagreement Onsite work in London preferred - remote work with visits in office once a month acceptable