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Salary: USD 160,000 - 190,000 / annual
Placer.ai is a location intelligence platform that helps organizations understand physical-world behavior and markets through advanced analytics. The company reached $100M ARR in 6 years, achieved unicorn status ($1B+ valuation) in 2022, and is recognized as one of North America's fastest-growing tech companies. Named a Forbes America's Best Startup Employer and Deloitte Technology Fast 500 company.
You will lead AI product solutions and development efforts, driving the technical vision and architecture of the AI-powered location intelligence platform. You'll build production-ready solutions that transform how businesses understand and leverage location data, overseeing full-stack development of conversational AI systems from LLM integration and agentic workflows to real-time streaming interfaces.
Key responsibilities include:
- Collaborate with product, design, and customers to define technical direction and build features end-to-end (APIs, data models, services, UI)
- Design and ship production AI features: LLM capabilities, retrieval systems, agents, tool-calling flows (including MCP), with proper permissions and failure recovery
- Build evaluation frameworks from real user questions to measure every prompt or model change before shipping
- Secure AI features against prompt injection, data leaks, and hallucinations
- Maintain production health through monitoring, tracing (including AI cost and latency), and debugging from real traces
- Use AI coding tools (Claude Code, Cursor, etc.) as core workflow and own quality, tests, and reliability
- Raise engineering standards through code review and team workflow improvements
- Turn vague requirements into clear technical plans and adjust as you learn
- Make architecture decisions balancing speed against maintainability
Requirements:
- 10+ years of software engineering shipping customer-facing products in production
- Strong full-stack fundamentals: Python (FastAPI or similar), React and TypeScript, relational databases and SQL, containerized cloud services
- Strong system design skills: data modeling, API design, production systems that scale reliably, fast, and securely; can explain trade-offs
- Hands-on experience shipping LLM-powered features: context engineering (prompts, retrieval, tools, memory), evals, handling model failures, AI system architecture
- AI-native workflow: plan before prompting, work in small reviewable steps, give agents good context, use tests as safety net, recognize when output is wrong
- Strong product sense: understand user problems, care about experience, push back when something doesn't feel right
- High emotional intelligence: listen well, read stakeholder needs, disagree while maintaining trust
- Clear communicator with technical and non-technical audiences
- Comfortable with ambiguity; work independently and know when to ask for help
Nice to haves:
- Demonstrated use of AI tools to work more efficiently (professionally or personally)
- Comfort integrating generative AI into workflows to boost productivity and quality
