Staff Software Engineer, AI
New
M
MapleHealthcare technology
Workplace type: RemoteFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience building and operating production software, with Staff-level scope or equivalent technical leadership
- Required Skills
- PostgreSQLLangChainDistributed Systems
Requirements
- Have 8+ years of experience building and operating production software, with Staff-level scope or equivalent technical leadership.
- Bring recent hands-on experience shipping AI- or LLM-powered product features as part of a product engineering team.
- Have experience taking ambiguous product problems through technical discovery, architecture, implementation, launch, and iteration.
- Demonstrate strong software engineering fundamentals across scalability, reliability, performance, security, and maintainability.
- Have hands-on coding experience in a modern production language and be able to work across AI services and product systems.
- Have experience designing APIs, data flows, services, integrations, or distributed systems that operate in production.
- Show a product mindset and focus on user outcomes and improving products over time.
- Be able to influence technical direction across teams without relying on formal authority.
- Have experience mentoring engineers and improving how teams design, build, test, and operate software.
- Communicate technical trade-offs clearly to technical and non-technical partners.
Responsibilities
- Own an AI product area from problem discovery and technical design through implementation, launch, monitoring, and iteration.
- Partner with Product and Design to identify user needs and turn ambiguous problems into useful experiences.
- Design and build production AI systems using LLMs, agents, retrieval, tool calling, workflow orchestration, and human-in-the-loop patterns.
- Write production code, review designs and pull requests, debug issues, and participate in operational ownership after launch.
- Establish evaluation and monitoring approaches for product quality, safety, reliability, latency, and cost.
- Partner with Platform Engineering on shared services, model providers, infrastructure, and ownership boundaries.
- Work with Security, Privacy, Legal, and Clinical stakeholders on responsible handling of PHI and safe AI deployment.
- Develop reusable engineering patterns and mentor engineers through collaboration, design reviews, documentation, and example-setting.
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