Software Engineer, Applied AI
Clay is hiring a Software Engineer, Applied AI to help build reliable AI agents and the shared platform they run on. The role focuses on turning agent capabilities into production-ready systems that deliver measurable task completion, reliability, and time saved.
About the role
Clay’s mission is to help organizations turn any growth idea into reality. The product is increasingly powered by AI agents that research, enrich, and take action on behalf of users. This role is a shared entry point across multiple teams working toward dependable agents that can run unattended in production. Clay has 11,000+ customers, 150+ integration partners, 125+ agencies, 50+ Clay clubs, and 30k members on Slack. The company raised a $100M Series C in 2025 backed by Sequoia, CapitalG, and First Round, crossed $100M in revenue in 2025, and announced a second employee tender offer in 9 months at a new $5B valuation in 2026. The company also launched a community equity round for customers, agency partners, and club members.
Responsibilities
- Work closely with product, research-adjacent teammates, and other engineers
- Improve agent reliability, steerability, and trustworthiness for real work
- Turn model improvements into measurable gains in task completion, reliability, and time saved
- Design and iterate on agent behavior across real GTM workflows
- Map manual multi-step workflows into agent-driven flows
- Build and run evals that verify task completion and catch regressions
- Analyze production failures and systematically improve robustness
- Take agent flows from prototype through closed beta to general availability
- Define what good looks like for each agent product or flow
- Build the core agent harness for shared platform use
- Improve agent performance through prompting, tool use, and context construction
- Design guardrails and safety checks for predictable production behavior
- Build a cross-surface evals framework
- Support teams building forks or variants of the managed agent
- Build feedback loops from usage and production logs into prompts, tools, and eval coverage
- Potential work on sourcing a TAM list by combining search, audience building, and enrichment into one flow
- Potential work on agent products or agent platform and infrastructure depending on team match
Requirements
- 3–5 years of experience building or shipping production systems with LLMs or agents
- Strong backend fundamentals: APIs, databases, distributed systems
- Experience with model or agent evaluation: designing evals, measuring regressions, or turning fuzzy quality questions into measurable signals
- Comfort debugging messy, real-world failures
- Bias toward shipping and iterating quickly in an evolving space
Skills
- LLM
- RAG
- React
- TypeScript
- Python
- AWS
- Aurora/PostgreSQL
- ECS/Fargate
- Lambda
- OpenSearch
- ElastiCache/Redis
- Terraform
- Agents
- Prompting
- Tool-use design
- Agent orchestration
- Retrieval
- Structured extraction
- Fine-tuning
- APIs
- Databases
- Distributed systems
- Model evaluation
- Agent evaluation
- Memory systems
- Tool infrastructure
- Retrieval architecture
- Guardrails
- Safety checks
- Feedback loops
Nice to have
- Experience with agent frameworks, tool-calling systems, or retrieval architectures
- Experience with vector search, hybrid search, or RAG
- Experience building or maintaining eval or benchmark infrastructure for LLM-based systems
- Experience running fine-tuning in production
- Experience with GTM, sales, or marketing workflows such as lead sourcing, enrichment, or audience building
- Familiarity with Clay's stack
- Growth mindset
Benefits
- Offers equity
- Employees can work for free with coaches specializing in creativity, management, and more
- Operating principles include negative maintenance and non-attached action
Pay
$172.3K–303.6K/yr
Schedule
Full-time, hybrid