Founding AI/ML Research Engineer
1 day ago
Newcastle upon Tyne
About A1 A1 is a self-funded, independent AI group, focused on building a new consumer AI application with global impact. We’re assembling a small, elite team of ML, engineering and product builders who want to work on meaningful, high-impact problems. 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, our data strategy, and our 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, • 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, • 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)