Founding Lead Machine Learning Engineer

A1; United States; Remote; USD 120,000 - 150,000; Full time

Job summary

A pioneering AI development company in the United States is seeking an innovative technical lead to shape the core technical direction of their AI application. You will design end-to-end training systems, adapt model architectures, and develop scalable inference frameworks. Ideal candidates will have strong deep learning backgrounds, experience with large model training, and robust software engineering capabilities. This role offers the opportunity to work on impactful AI projects in a collaborative environment.

Qualifications

• Strong background in deep learning and transformer architectures.
• Hands-on experience training or fine-tuning large models (LLMs or vision models).
• Strong software engineering skills—writing robust, production-grade systems.

Responsibilities

• Build end-to-end training pipelines: data → training → eval → inference.
• Design new model architectures or adapt open-source frontier models.
• Architect scalable inference systems using vLLM / TensorRT-LLM / DeepSpeed.

Skills

Deep learning

Transformer architectures

PyTorch

GPU optimization

Software engineering

Tools

TensorFlow

JAX

DeepSpeed

FSDP

Ray

Job description

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)