Founding Machine Learning Engineer

Transform language models into real-world, high-impact product experiences.

A1 is a self-funded AI group, operating in full stealth. We’re building a new global consumer AI application focused on an important but under explored use case.

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.

• You are creating the intelligence layer of A1’s first product, defining how it understands, reasons, and interacts with users.

• Your decisions shape our entire technical foundation — model architectures, training pipelines, inference systems, and long-term scalability.

• You will push beyond typical chatbot use cases, working on a problem space that requires original thinking, experimentation, and contrarian insight.

• You influence not just how the product works, but what it becomes, helping steer the direction of our earliest use cases.

• You are joining as a founding builder, setting engineering standards, contributing to culture, and helping create one of the most meaningful AI applications of this wave.

• 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 / Tensor
  
  RT-LLM / Deep Speed

• 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

• You take ownership - you solve problems end-to-end rather than wait for perfect instructions

• You learn through action - prototype → test → iterate → ship

• You’re calm in ambiguity - zero-to-one building energises you

• You bias toward speed with discipline - V1 now > perfect later

• You see failures and feedback as essential to growth

• You work with humility, curiosity, and a founder’s mindset

• You lift the bar for yourself and your teammates every day

• 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 Tensor Flow

• Experience with distributed training frameworks (Deep Speed, 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

• Experience with LLM inference frameworks (vLLM, Tensor
  
  RT-LLM, Faster Transformer)

• 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, Deep Mind, FAIR, Anthropic, OpenAI)

• Extreme ownership and autonomy from day one - you define and build key model systems.

• Founding-level influence over technical direction, model architecture, and product strategy.

• Remote-first flexibility

• High-impact scope—your work becomes core infrastructure of a global consumer AI product.

• Competitive compensation and…