Founding AI/ML Research Engineer

About A1A1 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 RoleYou 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 DoingBuild end-to-end training pipelines: data → training → eval → inferenceDesign new model architectures or adapt open-source frontier modelsFine-tune models using state-of-the-art methods (LoRA/QLoRA, SFT, DPO, distillation)Architect scalable inference systems using vLLM / TensorRT-LLM / DeepSpeedBuild data systems for high-quality synthetic and real-world training dataDevelop alignment, safety, and guardrail strategiesDesign evaluation frameworks across performance, robustness, safety, and biasOwn deployment: GPU optimization, latency reduction, scaling policiesShape early product direction, experiment with new use cases, and build AI-powered experiences from zeroExplore frontier techniques: retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, multimodal modelsWhat You'll NeedStrong background in deep learning and transformer architecturesHands-on experience training or fine-tuning large models (LLMs or vision models)Proficiency with PyTorch, JAX, or TensorFlowExperience with distributed training frameworks (DeepSpeed, FSDP, Megatron, ZeRO, Ray)Strong software engineering skills — writing robust, production-grade systemsExperience with GPU optimization: memory efficiency, quantization, mixed precisionComfortable owning ambiguous, zero-to-one technical problems end-to-endNice to HaveExperience with LLM inference frameworks (vLLM, TensorRT-LLM, FasterTransformer)Contributions to open-source ML librariesBackground in scientific computing, compilers, or GPU kernelsExperience with RLHF pipelines (PPO, DPO, ORPO)Experience training or deploying multimodal or diffusion modelsExperience in large-scale data processing (Apache Arrow, Spark, Ray)Prior work in a research lab (Google Brain, DeepMind, FAIR, Anthropic, OpenAI)