Founding Lead Machine Learning Engineer

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, 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) #J-18808-Ljbffr A1