9 days ago
London, United KingdomStaff+
Responsibilities
- Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment.
- Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.
- Architect and operate scalable inference systems, balancing latency, cost, and reliability.
- Design and maintain data systems for high-quality synthetic and real-world training data.
- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
- Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
- Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
Requirements
- Strong background in deep learning and transformer-based architectures.
- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
- Proficiency with at least one modern ML framework (e.g. PyTorch, JAX).
- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).
- Strong software engineering fundamentals for writing robust, maintainable systems.
- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.
- Comfort owning ambiguous, zero-to-one ML systems end-to-end.
- A bias toward shipping, learning fast, and improving systems through iteration.
Tech Stack
Apache SparkPyTorch
Categories
AI & MLData Engineering