Staff Software Engineer, AI
Empathy Talent
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Benefits offered by this job
Job summary
Empathy Talent is seeking a Staff Software Engineer, AI to define technical direction for an AI-powered product and platform, designing scalable systems for production-grade agents in enterprise environments.
This hands-on leadership role combines deep AI expertise with software engineering fundamentals, spanning backend, AI infrastructure, and orchestration, while mentoring engineers and shaping architecture across the org.
Qualifications
- 8+ years of production software engineering experience.
- Staff-level experience owning architecture across multiple teams.
- Deep expertise in TypeScript, Python, APIs, and distributed systems.
- Proven track record shipping LLM-powered applications.
Responsibilities
- Architect AI-native software systems for scalable enterprise use.
- Own end-to-end product areas from architecture to deployment.
- Develop agent orchestration, tool-calling workflows, and multi-step AI applications.
- Build and integrate AI infrastructure including prompts, tools, guardrails, and observability.
- Mentor engineers and raise the technical bar through reviews.
Skills
Tools
Job description
As a Staff Software Engineer, AI, you’ll help define the technical direction of the AI-powered product and platform—designing and building the software systems that enable highly reliable, production-grade AI agents in complex enterprise environments.
This is a hands-on Staff-level engineering role for someone who combines exceptional software engineering fundamentals, deep AI expertise, strong product instincts, and technical leadership. You’ll work across the stack—from distributed backend systems and AI infrastructure to agent orchestration and customer-facing product experiences—while influencing architecture and engineering standards across the organization.
Compensation: Up to $310,000 base + equity
- Design and build scalable software architecture supporting AI-powered products and agentic workflows
- Set technical direction for critical components across the AI application and platform stack
- Build reliable distributed systems capable of supporting complex, enterprise-grade AI workloads
- Identify architectural and engineering bottlenecks impacting reliability, scalability, and development velocity
- Make high-leverage technical decisions and remain hands-on through implementation and production
- Own complex product areas end-to-end—from architecture and implementation through deployment and iteration
- Build agent orchestration, retrieval, workflow, and reasoning systems capable of handling real-world enterprise complexity
- Develop the infrastructure surrounding LLMs, including model integrations, prompts, tools, guardrails, evaluations, observability, and feedback loops
- Build APIs, backend services, data pipelines, and platform capabilities that power AI-native product experiences
- Rapidly prototype new approaches, validate them with real users, and harden successful solutions for production
- Debug difficult issues spanning application code, AI behavior, infrastructure, and distributed systems
- Partner with Engineering, Product, and Design leadership to translate product strategy into scalable technical architecture
- Establish engineering patterns and standards for building reliable AI-powered software
- Create reusable platforms, abstractions, APIs, and tooling that increase engineering velocity across the organization
- Influence technical decisions across multiple teams and product areas
- Mentor senior engineers and raise the technical bar through architecture reviews, code reviews, and hands-on collaboration
- Balance long-term architectural quality with the speed required to ship and learn
- Stay at the frontier of LLMs, agents, applied AI, and AI-native software development
- Rapidly evaluate new models, frameworks, infrastructure, and techniques for practical production use
- Improve how AI systems are evaluated, monitored, debugged, and operated in production
- Develop internal knowledge and best practices around building reliable AI applications
- Help establish the organization as a technical leader in enterprise and vertical AI
You’re a Staff-level software engineer who happens to be deeply experienced in AI—not an AI researcher who occasionally writes production code.
We care more about capability and trajectory than checking every box, but strong candidates will typically bring:
- 8+ years of production software engineering experience, with significant experience building AI/ML-powered products or platforms
- Staff-level experience owning architecture and complex technical systems across multiple teams or product areas
- Deep expertise in TypeScript, Python, backend engineering, APIs, and distributed systems
- Proven experience designing and shipping LLM-powered applications into production
- Experience building agentic systems, orchestration layers, tool-calling workflows, and multi-step AI applications
- Strong knowledge of RAG, retrieval architectures, vector databases, embeddings, and data pipelines
- Hands-on experience with modern LLM APIs and ecosystems such as OpenAI, Anthropic, Gemini, LangGraph, or similar technologies
- Experience designing evaluation frameworks, observability systems, guardrails, and reliability infrastructure for AI applications
- Strong understanding of traditional software reliability alongside the unique failure modes introduced by probabilistic AI systems
- Experience designing scalable services and systems for enterprise customers
- Track record of influencing technical direction and mentoring experienced engineers
- AI-native product engineering: Building products where AI is fundamental to the architecture rather than an added feature
- Enterprise-grade reliability: Turning probabilistic AI capabilities into software professionals can depend on
- Agentic systems: Building agents capable of reasoning, retrieving information, using tools, and completing complex workflows
- Human-in-the-loop systems: Determining where automation creates leverage and where expert judgment should remain involved
- Nuanced evaluation: Measuring quality when there isn’t always a single objectively correct answer
- Explainability: Making AI behavior transparent, debuggable, and trustworthy
- Complex domains: Building elegant software for environments involving compliance, security, and enterprise rigor
- Shipping real value: Moving quickly from prototype to production and building AI experiences customers actively rely on
- Comprehensive health and wellness benefits
- Flexible time off and work schedules
- Technology reimbursements
- 401(k) plan
- Twice-yearly in-person offsites across the U.S.
- Wellness benefits starting on your first day
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