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Senior ML Engineer (ML/AI)

About Lyra Health

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Lyrahealth Source published Jun 10, 2026 Verified 6 days ago
✓ 100% verification score · Source: Lyrahealth (lever) · Always confirm final requirements on the original source.
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EmploymentFull-time
Work modeRemote / location-flexible

Overview

About Lyra Health

Full job description

About Lyra Health Lyra Health is a leading provider of evidence-based mental health care, serving more than 20 million people globally in partnership with employers and more than 100 million through health plan and partner relationships. The company has delivered more than 15 million sessions of mental health care, published more than 35 peer-reviewed studies, and delivered unmatched outcomes in terms of access, clinical effectiveness, and cost efficiency. Extensive peer-reviewed research confirms Lyra’s transformative care model helps people recover twice as fast and results in a 26% annual reduction in overall healthcare claims costs. Lyra is transforming access to life-changing mental health care through Lyra Empower, the only fully integrated, AI-powered platform combining the highest-quality care and technology solutions.

About the Role 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.

Lyra is for you if you: 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

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.

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

Experience defining and using Protobuf messages

Experience working with Docker and deploying applications to Kubernetes

Experience with relational and low-latency databases

Experience working with Celery

Experience building RAG (retrieval-augmented generation) based solutions

Experience setting up and maintaining vector databases

Experience writing production code in Java/Kotlin

Experience building solutions on cloud infrastructure, particularly AWS

Experience working with highly sensitive data in a healthcare environment

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