Founding Machine Learning Engineer
A1
- London, England, United Kingdom
- London, England, United Kingdom
About
About The Role You will shape the core technical direction of A1—model selection, training strategy, infrastructure, and long‑term architecture. This is a founding technical role: your decisions will define our model stack, data strategy, and product capabilities for years ahead. You won’t just fine‑tune models—you’ll design systems: training pipelines, evaluation frameworks, inference stacks, and scalable deployment architectures. You will have full autonomy to experiment with frontier models (LLaMA, Mistral, Qwen, Claude‑compatible architectures) and build new approaches where existing ones fall short.
What You’ll be Doing
Build end‑to‑end training pipelines: data → training → eval → inference
Design new model architectures or adapt open‑source frontier models
Fine‑tune models using state‑of‑the‑art methods (LoRA/QLoRA, SFT, DPO, distillation)
Architect scalable inference systems using vLLM / TensorRT‑LLM / DeepSpeed
Build data systems for high‑quality synthetic and real‑world training data
Develop alignment, safety, and guardrail strategies
Design evaluation frameworks across performance, robustness, safety, and bias
Own deployment: GPU optimization, latency reduction, scaling policies
Shape early product direction, experiment with new use cases, and build AI‑powered experiences from zero
Explore frontier techniques: retrieval‑augmented training, mixture‑of‑experts, distillation, multi‑agent orchestration, multimodal models
What You’ll Need
Strong background in deep learning and transformer architectures
Hands‑on experience training or fine‑tuning large models (LLMs or vision models)
Proficiency with PyTorch, JAX, or TensorFlow
Experience with distributed training frameworks (DeepSpeed, FSDP, Megatron, ZeRO, Ray)
Strong software engineering skills—writing robust, production‑grade systems
Experience with GPU optimization: memory efficiency, quantization, mixed precision
Comfortable owning ambiguous, zero‑to‑one technical problems end‑to‑end
Nice to Have
Experience with LLM inference frameworks (vLLM, TensorRT‑LLM, FasterTransformer)
Contributions to open‑source ML libraries
Background in scientific computing, compilers, or GPU kernels
Experience with RLHF pipelines (PPO, DPO, ORPO)
Experience training or deploying multimodal or diffusion models
Experience in large‑scale data processing (Apache Arrow, Spark, Ray)
Prior work in a research lab (Google Brain, DeepMind, FAIR, Anthropic, OpenAI)
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Languages
- English
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