Principal Machine Learning Engineer
Location: London, UK (Hybrid)
Employment Type: Full-Time
Department: Engineering
About the Company
The company is building an AI-native smart assistant designed to help everyday users manage conversations, tasks, organisation, and workflows with minimal prompting.
The product focuses on creating reliable AI systems that can support long-running workflows, persistent context, multi-step reasoning, external tool interaction, and real-world task completion. The goal is to help users complete everyday tasks significantly faster through intelligent automation.
The Role
As a Principal Machine Learning Engineer, you will be responsible for turning research direction into production-grade machine learning systems.
This role owns the execution layer of the company’s intelligence platform, covering training pipelines, inference systems, evaluation tooling, and deployment. You will work on building scalable, reliable ML infrastructure that can support real-world AI product experiences.
Key Responsibilities
- Build and own end-to-end ML pipelines across data, training, evaluation, inference, and deployment.
- Fine-tune and adapt models using modern techniques 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.
- Build evaluation pipelines covering performance, robustness, safety, and bias in partnership with research leadership.
- Own production deployment, including GPU optimisation, memory efficiency, latency reduction, and scaling policies.
- Collaborate closely with application engineering teams to integrate ML systems into backend, mobile, and desktop products.
- Make pragmatic trade-offs and ship improvements quickly based on real user feedback.
- Work within real production constraints including latency, cost, reliability, and safety.
Technical Skills
- Strong background in deep learning and transformer-based architectures.
- Hands‑on experience training, fine‑tuning, or deploying large‑scale machine learning models in production.
- Proficiency with at least one modern ML framework such as PyTorch or JAX.
- Experience with distributed training and inference frameworks such as DeepSpeed, FSDP, Megatron, ZeRO, or Ray.
- Strong software engineering fundamentals, with the ability to write robust, maintainable, production‑grade systems.
- Experience with GPU optimisation, including memory efficiency, quantisation, and mixed precision.
Personal Attributes
- Comfortable owning ambiguous, zero‑to‑one ML systems end‑to‑end.
- Strong bias toward shipping, learning quickly, and improving systems through iteration.
- Able to exercise sound judgement and work independently in a fast‑moving environment.
Ideal Experience
- LLM inference frameworks such as vLLM, TensorRT‑LLM, or FasterTransformer.
- Open‑source contributions to machine learning or systems libraries.
- Scientific computing, compilers, or GPU kernels.
- RLHF pipelines including PPO, DPO, or ORPO.
- Training or deploying multimodal or diffusion models.
- Large‑scale data processing tools such as Apache Arrow, Spark, or Ray.
Working Environment
The company believes the best products are built by small, world‑class teams with high talent density. The team works collaboratively, moves quickly, and balances high‑quality delivery with continuous learning.
Team members are expected to bring structure, exercise judgement, and execute independently while contributing to a product designed to deliver practical AI benefits at global scale.
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