Machine Learning Engineer

We're an AI-first startup building products on top of large language models and custom ML pipelines. The research side is moving fast — we have models that work in notebooks and prototypes that prove the concepts. The gap is production: getting models out of research environments and into systems that run reliably at scale without falling over.

We're looking for a Machine Learning Engineer who sits at the intersection of research and engineering — someone who understands the models well enough to collaborate with the research team, and has the engineering discipline to build production systems that don't require constant babysitting. You'll work directly with the research team and the product team and have significant ownership over the ML infrastructure.

What you'll work on

• Taking trained models from research environments to production — building serving infrastructure, API layers, and monitoring pipelines
• Fine-tuning and adapting large language models for specific use cases using proprietary datasets — LoRA, PEFT, and full fine-tuning where appropriate
• Building and maintaining ML pipelines for data preprocessing, training runs, evaluation, and deployment
• Implementing model monitoring — tracking performance drift, latency degradation, and failure cases in production
• Working with the research team to evaluate new model architectures and techniques against real production constraints
• Optimizing inference performance — reducing latency and cost without sacrificing output quality

Requirements

• 3+ years of machine learning engineering experience with production deployments — research or notebook-only experience doesn't qualify
• Strong Python and PyTorch for model training, evaluation, and inference
• Hands-on LLM fine-tuning experience — LoRA, PEFT, or full fine-tuning on domain-specific datasets
• MLflow or equivalent for experiment tracking, model registry, and deployment workflows
• US timezone required for daily collaboration with research and product