Staff Software Engineer - Internal Efficiency

Lyra Health provides evidence-based mental health care through an integrated technology platform. The Staff Software Engineer will embed with internal teams to identify workflow inefficiencies and build, deploy, and iterate on AI-powered tools, agents, and automations. The role focuses on measurable adoption and efficiency gains while collaborating across engineering, data science, product, security, and healthcare teams.

Responsibilities:

• Embed with internal teams across the company to map their workflows, identify bottlenecks, and scope AI-driven solutions with clear, measurable efficiency outcomes
• Take ownership of the full lifecycle: from discovery and prototyping through deployment, adoption, measurement, and iteration
• Build and continuously improve AI-powered tools, agents, and automations, integrating with our Python and Java-based microservices, our web and mobile apps, and third-party systems
• Drive the continued integration of agentic software development across engineering, expanding adoption, elevating our technical capabilities, and acting as a force for innovation
• Evaluate emerging AI tools, models, and patterns; run fast experiments and convert the winners into durable internal capabilities
• Define and track the metrics that prove impact (time saved, throughput, quality, cost) and communicate results to stakeholders and leadership
• Work with fellow engineering team members to collaboratively create and refine technical designs and solutions
• Collaborate with key partners including data science, product management, design, security, and operational and behavioral health teams
• Follow and promote best practices during all phases of software development, adhering to the highest quality and security standards, including the responsible handling of sensitive data in AI workflows

Requirements:

• BS in CS or related technical field
• 8+ years of experience developing enterprise or consumer web applications, ideally using Python or Java/Spring
• Hands-on experience building with LLMs in production: agentic tools and frameworks, prompt and context engineering, evals, and development workflows using Claude and Codex
• Demonstrated ability to work directly with non-engineering stakeholders: eliciting requirements, translating ambiguous business problems into shipped software, and driving adoption of what you build
• Strong judgment about where AI genuinely helps versus where it adds risk or noise, especially in a healthcare context
• Must be highly proactive and a self-starter