Staff ML Engineer (ML/AI)

Responsibilities

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.

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.

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.

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.

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.

Hands-On Leadership: Lead by example through technical prototypes, critical-path architecture, and strategic coding contributions.

And of course, you will be coding!

Qualifications

8+ years of experience deploying complex ML/AI solutions into mission-critical production environments, with a proven track record of technical leadership.

Deep System & Software Engineering Expertise: Mastery of Python, RESTful API design, Protobuf, and microservices architecture.

Production AI/ML Infrastructure Mastery: Deep expertise with Docker, Kubernetes, container orchestration, and real-time inference services.

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.

Data Layer Systems: Expertise with relational databases, low-latency key-value stores, distributed queueing system architectures (e.g., Celery, Kafka), and data pipeline orchestration.

Cloud Architecture: Strong experience architecting cloud-native solutions on AWS (or equivalent cloud providers).

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

Polyglot Engineering Background: Experience writing high-performance production code in Java or Kotlin.

Healthcare & Sensitive Data Expertise: Experience architecting AI/ML systems within highly regulated environments (HIPAA compliance, SOC2, handling PHI/PII).

MLOps / Platform Productization: Experience building internal developer platforms or ML tooling used by dozens of data scientists and engineers.