Staff ML Engineer (ML/AI)
Summary
Lead the architecture of Lyra’s AI/ML platform, designing scalable generative-AI systems, RAG pipelines, and guardrails for mental-health care delivery.
About the Role
We are seeking a Staff ML/AI Engineer to define and drive the architectural vision for Lyra’s machine learning and generative AI technology landscape. In this role, you will serve as a technical anchor across the engineering and data organizations—architecting enterprise-scale AI platforms, setting technical strategy for high-impact AI/ML initiatives, and ensuring our AI products operate with top-tier reliability, security, and medical precision.
The ideal candidate is a seasoned technical leader who excels at translating complex healthcare challenges into scalable platform solutions, building consensus across cross-functional leadership, and elevating the technical bar for the entire engineering organization.
Lyra is for you if you
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Thrive on working with brilliant teammates to solve complex, meaningful problems
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Are passionate about making a social impact and supporting people at their most challenging moments
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Enjoy cross-functional collaboration with physicians, therapists, data scientists, data analysts and product managers
Responsibilities
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Drive AI Platform Architecture: Design and execute the long-term roadmap for Lyra’s machine learning and generative AI platform, enabling fast, safe, and reliable deployment of frontier models across the company.
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Lead AI Infrastructure Vision: Architect end-to-end training, fine-tuning, and low-latency inference platforms, including centralized RAG architecture, vector databases, and enterprise evaluation/guardrail frameworks.
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Set Engineering Excellence Standards: Establish organizational standards for the full AI/ML SDLC—from dataset lineage and CI/CD pipelines to automated model evaluation, red-teaming, and production monitoring.
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Cross-Functional Technical Leadership: Partner closely with Product Management, Data Science, Security, and Clinical leaders to translate strategic clinical goals into foundational AI capability roadmaps.
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Mentor and Multiply Impact: Elevate the engineering culture by mentoring Senior ML Engineers, conducting high-leverage architectural reviews, and establishing engineering best practices across teams.
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Hands-On Leadership: Lead by example through technical prototypes, critical-path architecture, and strategic coding contributions.
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And of course, you will be coding!
Qualifications
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8+ years of experience deploying complex ML/AI solutions into mission-critical production environments, with a proven track record of technical leadership.
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Deep System & Software Engineering Expertise: Mastery of Python, RESTful API design, Protobuf, and microservices architecture.
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Production AI/ML Infrastructure Mastery: Deep expertise with Docker, Kubernetes, container orchestration, and real-time inference services.
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Modern Generative AI Architecture: Hands-on experience designing and deploying RAG pipelines, fine-tuning LLMs, managing vector databases, and building robust LLM evaluation/guardrail frameworks.
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Data Layer Systems: Expertise with relational databases, low-latency key-value stores, distributed queueing system architectures (e.g., Celery, Kafka), and data pipeline orchestration.
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Cloud Architecture: Strong experience architecting cloud-native solutions on AWS (or equivalent cloud providers).
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Strategic Communication: Exceptional ability to distill highly ambiguous technical problems into clear strategic priorities and influence leadership across engineering, product, and business domain disciplines.
Preferred Qualifications
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Polyglot Engineering Background: Experience writing high-performance production code in Java or Kotlin.
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Healthcare & Sensitive Data Expertise: Experience architecting AI/ML systems within highly regulated environments (HIPAA compliance, SOC2, handling PHI/PII).
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MLOps / Platform Productization: Experience building internal developer platforms or ML tooling used by dozens of data scientists and engineers.
Skills
As published by lever · 8 questions · 3 written answers
Basics
Resume/CV, Full name, Pronouns, Email, Phone, Current location, Current company, LinkedIn URL, Twitter URL, GitHub URL, Portfolio URL, Other website, To which gender identity do you most identify?, To which sexual orientation do you most identify?, Do you identify as a person living with a disability?, Are you fluent in any of the following languages?, Do you identify as LGBTQIA+?
Pick from a list (5)
- Are you legally authorized to work in the United States for our Company?
- Do you now, or will you in the future, require sponsorship for employment visa status (e.g., H-1B visa status, etc.) to work legally for our Company in the United States?
- Are you an employee of Lyra, Bend or an affiliated company (Currently or Previously)?
- If yes to the previous question, please select your affiliation: optional
- If you are a Lyra employee, do you certify you have reviewed the Internal Mobility Policy and meet the requirements? optional
Written answers (3)
- If you selected "other", please share your relationship here optional
- What specific strategies (such as quantization, distillation, or guardrailing) have you implemented to optimize model latency, reduce costs, and mitigate bias or safety concerns in production?
- Describe a production RAG system or GenAI solution (e.g., LLMs, fine-tuning, or vector retrieval) you designed and deployed. Which vector database did you use, and how did you approach indexing, retrieval quality, and latency optimization?