lever
Sr. Software Engineer - Internal Efficiency
Lyra Health
Remote, USA
Full-time
Senior · 6+ years experience
Remote
Discovered 1 weeks ago
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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
Qualifications
- BS in CS or related technical field
- 6+ 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
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