Senior Machine Learning Engineer, Lyra Health

• Thrive on working with brilliant teammates to solve complex, meaningful problems
• Are passionate about making a social impact and supporting people at their most challenging moments
• Enjoy cross-functional collaboration with physicians, therapists, data scientists, data analysts and product managers
• 6+ years of experience deploying ML/AI solutions in production environments
• Ability to write high-quality code in Python
• Experience building RAG (retrieval-augmented generation) based solutions
• Experience setting up and maintaining vector databases
• A strong desire to work on ML/AI based products
• A desire to learn new technologies quickly
• A thoughtful approach to balancing quality and deadlines in fast-paced settings
• Excellent communication skills with a talent for building consensus and alignment
• Strong organizational skills and the ability to distill complex problems into clear priorities that move the team and business forward

Desirable

• We are looking for an experienced Senior AI/ML Engineer to spearhead the design, development, and deployment of next-generation AI capabilities for our platform
• In this role, you won't just train models in a sandbox—you will architect robust, scalable production systems that integrate predictive analytics, Natural Language Processing (NLP), and Generative AI to solve complex, real-world problems
• As a senior member of the team, you will bridge the gap between data science research and production-grade software engineering, while mentoring junior engineers and influencing our AI product roadmap
• Design, train, fine-tune, and evaluate deep learning, classical ML, and foundational GenAI models (LLMs, diffusion models) to drive core product features
• Build, scale, and maintain robust ML pipelines for continuous training, evaluation, and real-time/batch inference using modern MLOps frameworks
• Collaborate with backend and frontend teams to integrate AI models into microservices, ensuring low latency, high availability, and optimal resource utilization
• Architect scalable data pipelines for preprocessing, vectorizing, and ingestion of massive structured and unstructured datasets
• Implement rigorous evaluation frameworks for model alignment, bias mitigation, guardrailing, and cost/latency optimization (e.g., quantization, distillation)
• Provide technical leadership, conduct thorough code reviews, and mentor junior/mid-level engineers on best practices in software craftsmanship and ML engineering