Top Tech Jobs & Startup Jobs in Boston, MA

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Build and deploy AI-powered tools, agents, and automations that improve internal workflows across Lyra Health. Partner with business and clinical teams to identify opportunities, translate ambiguous needs into software, and own solutions from discovery through adoption and measurable impact. Integrate AI capabilities with Python and Java microservices, web and mobile applications, and third-party systems. Evaluate emerging AI tools, promote agentic development practices, track efficiency metrics, and ensure responsible handling of sensitive healthcare data.

Embed with internal teams to map workflows and build AI-powered tools, agents, and automations. Own full lifecycle from discovery to deployment, adoption, measurement, and iteration. Integrate solutions with Python/Java microservices, web and mobile apps, and third-party systems. Run experiments on emerging models and agentic frameworks, track efficiency metrics, and collaborate with product, design, data science, security, and clinical partners.

Build and maintain backend services and infrastructure to support core ML/AI product features. Design scalable, resilient, high-performance systems, implement production REST APIs, deploy and manage applications (Docker/Kubernetes), and collaborate cross-functionally while coding daily.

As a Sr. Data Engineer at Lyra Health, you will build and scale core data infrastructure, design data warehouses, and create efficient data pipelines while collaborating across departments.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

Design and evaluate predictive and generative models, extract insights from disparate clinical and product data, build and partner on data pipelines, run rigorous evaluations and experiments, and deliver time-sensitive ad hoc analyses to product, engineering, and clinical stakeholders.