Machine Learning Engineer

Invitae is seeking an experienced Machine Learning Engineer to join our Applied AI team. Our team has successfully launched real-time APIs for model inference across diverse inputs, and we're poised to tackle new use cases that will improve patient experiences, enhance operational efficiency, and introduce transformative products. In this role, you will leverage your expertise in Python, machine learning infrastructure, and model development to solve complex business problems and develop production-ready ML models that directly contribute to improving patient outcomes and advancing healthcare.

What You’ll Do

• Design, develop, test, deploy, maintain, and improve machine learning models and systems.
• Collaborate with cross-functional teams to understand business requirements and translate them into ML solutions.
• Monitor, analyze, and improve the performance of deployed models and integrations based on user feedback and advancements in the field.
• Implement advanced machine learning techniques to solve business problems and enhance our genetic testing capabilities.
• Ensure the scalability, reliability, and security of ML infrastructure.

What You Bring

• Typically a minimum of 5 years of software development experience with a Bachelor’s degree; or 3 years and a Master’s degree; or 1 year and a PhD in a related field (AI, CS, software engineering).
• Proficiency in one or more technical stacks from infrastructure to application code, as well as training and evaluation frameworks within the ML engineering space. (Our stack includes: AWS, Azure, Python, PyTorch, TensorFlow, spaCy, FastAPI, KServe, Kubernetes, GitHub Actions, and New Relic.)
• Strong problem-solving and analytical skills.
• Experience with data preprocessing, feature engineering, model training and model serving.
• Ability to collaborate effectively with cross-functional teams.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Advanced degree in Computer Science, Machine Learning, or a related field.
• Experience with large language models (LLMs) and natural language processing (NLP) techniques.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Familiarity with MLOps best practices and tools for model versioning and deployment.
• Knowledge of genetics or experience in the healthcare domain.