Senior ML Engineer

• 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:

• You’ll 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.

• 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