Senior Software Engineer, Placer Intelligence
New
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Placer.aiLocation intelligence
United States, RemoteFull-TimeSenior
Salary160,000 - 190,000 USD per year
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Job Details
- Experience
- 10+ years of software engineering
- Required Skills
- PythonSQLTypeScriptFastAPIReact
Requirements
- Have 10+ years of software engineering experience shipping customer-facing products in production.
- Bring strong full-stack fundamentals, including Python with FastAPI or similar, React, and TypeScript.
- Have experience with relational databases and SQL.
- Have shipped containerized services to the cloud.
- Bring strong system design skills, including data modeling, API design, and reliable, fast, secure production systems.
- Have hands-on experience shipping LLM-powered features, including context engineering, prompts, retrieval, tools, memory, evaluations, model-failure handling, and AI system architecture.
- Use an AI-native workflow: plan before prompting, work in reviewable steps, provide agents with context, and use tests as a safety net.
- Demonstrate product sense and an understanding of user problems and product experience.
- Listen to stakeholders, understand their needs, and disagree while maintaining trust.
- Communicate clearly with technical and non-technical people.
- Work independently through ambiguity and know when to ask for help.
Responsibilities
- Work with product, design, and customers to shape what to build and how.
- Turn vague requirements into technical plans and adjust those plans as you learn.
- Design, build, and ship production features across APIs, data models, services, and UI.
- Make architecture decisions that balance speed with long-term maintainability.
- Build AI-powered product capabilities using LLMs, retrieval, agents, and tool-calling flows, including MCP.
- Build evaluations from real user questions to measure prompt and model changes before release.
- Protect AI features against prompt injection, personal-data leaks, and fabricated answers.
- Monitor and trace production systems, including AI cost and latency, and debug and fix issues.
- Use AI coding tools as part of development and own the quality, tests, and reliability of shipped work.
- Contribute to code reviews and improve the team's use of AI tools and engineering workflows.
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