Software Engineer

ABOUT PLACER.AI: 

Placer.ai is transforming how organizations understand the physical world. Our location analytics platform provides unprecedented visibility into locations, markets, and consumer behavior. Placer empowers thousands of customers—from Fortune 500 companies, to local governments and nonprofits— to make smarter, data-driven decisions.

What sets us apart? We've built the most advanced location intelligence platform in the market while maintaining an uncompromising commitment to privacy, proving that powerful analytics and responsible data practices can coexist.

Our growth reflects the market's demand: we reached $100M in annual recurring revenue within just 6 years of launching, achieved unicorn status with a $1B+ valuation in 2022, and continue to expand rapidly as one of North America's fastest-growing tech companies. We're creating a $100B+ market opportunity, and we're just getting started.

Named one of Forbes America's Best Startup Employers and a Deloitte Technology Fast 500 company, we're building a culture where innovation thrives, collaboration is the norm, and every team member contributes to reshaping how the world understands location.

SUMMARY:

We are seeking an experienced Software Engineer,  with deep GenAI expertise to lead our AI product solutions and development efforts. In this role, you will be responsible for driving the technical vision and architecture of our AI-powered location intelligence platform, building production-ready solutions that transform how businesses understand and leverage location data. You will oversee the full-stack development of our conversational AI system, from LLM integration and agentic workflows to real-time streaming interfaces, while establishing the technical foundation for our next-generation business intelligence tools. If you are a technical leader with 10+ years of software development experience, proven success in production GenAI environments, and a passion for building scalable AI platforms that deliver measurable business impact, we want to hear from you.

RESPONSIBILITIES:

• Work closely with product, design, and customers. Bring an engineer's view of what to build and how, not just take tickets.
• Turn vague requirements into clear technical plans, and adjust as you learn.
• Design, build, and ship production features across the full stack: APIs, data models, services, and UI.
• Make architecture decisions that balance speed now against maintainability later.
• Build AI-powered capabilities into the product: LLM features, retrieval, agents, and tool-calling flows (including MCP), with scoped permissions and failure recovery.
• Build evals from real user questions so every prompt or model change is measured before it ships.
• Protect AI features against prompt injection, personal-data leaks, and made-up answers.