Senior Software Engineer, Applied AI

Responsibilities

Build the Agent Platform and Tooling

• Design and implement orchestration, tool/function calling, evaluation harnesses, prompt/version management, tracing/observability, and safety/guardrails.
• Support retrieval-augmented generation (RAG), structured extraction, and production inference workflows.

Operationalize AI in the SDLC

• Embed AI into engineering workflows, including code review assistance, test generation, incident support, and developer copilots.
• Establish quality gates and measure impact.
• Determine which work to delegate to agents versus retain as human work, and scope tasks so agents can succeed.

Ship Applied AI Features End-to-End

• Own projects from prototype through production, including data needs, system design, model/vendor selection, rollout plans, monitoring, and iteration.
• Partner with product, design, and data/ML stakeholders to deliver customer-facing outcomes.

Diagnose and Drive AI Adoption

• Diagnose AI adoption bottlenecks and failure modes across teams.
• Build missing capabilities, teach existing tools, and advocate for adoption.

Mentor and Set Engineering Standards

• Establish practices for reliability, evaluation, incident response, privacy/security, and AI development.
• Coach engineers through design reviews, pairing, and technical leadership.
• Help engineers critically review AI-generated code.

Requirements

• 6+ years of professional software engineering experience or equivalent, including shipping production systems.
• Direct experience on an Applied AI or product AI team building LLM- or ML-powered features; agent or tooling experience is strongly preferred.
• Demonstrated judgment in agent-driven development, including delegation, agent scoping, context management, and output verification.
• Strong backend and systems design skills, including APIs, distributed systems, queues/workflows, observability, and performance.
• Ability to navigate ambiguity and drive outcomes with product, design, data/ML, and platform partners.
• Track record of mentoring peers and improving engineering quality.

Nice to Have

• Experience building platform capabilities for engineers, such as SDKs, internal frameworks, or developer tooling.
• Practical experience with embeddings, search/retrieval, evaluation methodologies, and model monitoring.
• Experience building AI-powered tooling or products such as agents and assistants at scale.
• Familiarity with inference constraints, including latency, cost, and caching; vendor/model tradeoffs; and deployment patterns.

Example Projects

• Agent orchestration layer with tool routing, policy guardrails, and traceability.
• RAG service with document ingestion, chunking/indexing, evaluation, and freshness controls.
• AI-in-SDLC rollout with automated pull-request review feedback and test-plan suggestions.
• Team-wide evaluation harness with goldens, regression tests, offline scoring, and online experimentation.
• Organization-wide AI adoption diagnostic and tooling-gap remediation plan.

Compensation and Benefits

• Typical salary range: $175,000–$185,000 USD annually.
• Company-subsidized medical, dental, and vision plans.
• 401(k) plan with company match.
• Annual bonus.
• Flexible paid time off.
• Paid leave programs, including 16-week paid parental leave and disability benefits.
• Workplace flexibility and modern work schedules.
• Company-wide in-person events and team outings.
• Lifestyle enhancement program.
• Company-provided equipment, with Windows and Mac options.
• Annual performance reviews and career development opportunities.