Machine Learning Engineer

• 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

Founding Machine Learning Engineer

Founding AI Engineer