ML Engineer: Code Intelligence & LLM Systems

Stealth Startup; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Stealth Startup is seeking a Machine Learning Engineer to build production-ready AI systems powering automated code review and developer productivity. You will work across the full ML lifecycle—from research and data prep to training, deployment, and inference optimization.

This role blends LLMs, deep learning, software engineering, and systems thinking to solve real-world problems for developers worldwide.

Qualifications

• 3+ years of Machine Learning or Applied AI experience.
• Hands-on experience with PyTorch or JAX.
• Deep understanding of Transformers, LLMs, and modern deep learning.
• Experience with RAG, embeddings, and retrieval systems.
• Model training, fine-tuning, evaluation, and experimentation.
• MLOps, model serving, and production deployment experience.
• Strong software engineering and problem-solving ability.

Responsibilities

• Design, train, and fine-tune Transformer and LLM-based models for code understanding.
• Build intelligent retrieval and RAG systems for large software repositories.
• Develop datasets, benchmarks, and evaluation frameworks to measure model quality.
• Deploy scalable ML models into production with reliable inference pipelines.
• Optimize model performance using techniques such as LoRA, distillation, and quantization.
• Collaborate with product and systems engineers to ship AI features used by developers worldwide.
• Research and implement state-of-the-art approaches in code intelligence and software engineering AI.

Skills

ML/AI experience

PyTorch

JAX

Transformers/LLMs

RAG & retrieval

Model training/fine-tuning

MLOps & production

Software engineering

Problem solving

Tools

PyTorch

JAX

CUDA

Job description

Stealth Startup is seeking a Machine Learning Engineer to build production-ready AI systems powering automated code review and developer productivity. You will work across the full ML lifecycle—from research and data prep to training, deployment, and inference optimization.

This role blends LLMs, deep learning, software engineering, and systems thinking to solve real-world problems for developers worldwide.