Founding AI/ML Research Engineer
il y a 4 semaines
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 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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