Machine Learning Engineer
Core
Own the end-to-end ML lifecycle from dataset creation and foundational research to building and deploying production-grade models, with a focus on Large Language Models (LLMs) and novel solution development.
Role type
Machine Learning Engineer (LLM focus)
Builds
Production-grade ML models, efficient inference pipelines, and novel datasets
Domain
Healthcare technology / Large Language Models (LLMs)
Deliverable
production ML models
Required skills
Python, PyTorch, systematic hyperparameter searching, LLM fine-tuning, prompt engineering, RAG, model optimization (quantization, pruning), dataset curation
Preferred skills
Transformers, Mixtures of Experts (MoE), open-source ML contributions, MLOps tools (Docker, Kubernetes, Kubeflow, Prometheus)
Technologies
MLflow, Weights & Biases, Optuna, Ray Tune, bitsandbytes, CI/CD, Docker, Kubernetes, Kubeflow, Prometheus
Responsibilities
Research and implement novel machine learning solutions; Build and manage efficient pipelines for rapid experimentation; Design, execute, and track experiments; Deploy models into production environments; Implement monitoring systems to track model performance and detect drift; Optimize models for inference speed, memory footprint, and cost; Lead efforts in dataset creation, augmentation, and curation
Seniority
Mid-level (3+ years experience)