# Software Engineer, Applied AI

**Company:** [Clay](https://hotfix.jobs/companies/clay)
**Location:** New York, NY
**Role:** ML Engineering
**Salary:** $170k – $300k/yr
**Skills:** LLMs, AI Agents, Python, TypeScript, React, APIs, Databases, Distributed Systems, Retrieval-Augmented Generation, Vector Search, AWS, Terraform, Datadog, Redis, Opensearch
**Posted:** 2026-08-14

> Build and ship production AI agents and the platform infrastructure that makes them reliable, steerable, and measurable. The role requires strong backend fundamentals, production LLM or agent experience, and expertise in evaluations, retrieval, orchestration, or tool-use design.

## Job Description

## Responsibilities

### Agent products
- Design and iterate on agent behavior across real go-to-market workflows, such as sourcing a total addressable market list through search, audience building, and enrichment.
- Map manual, multi-step workflows and turn them into agent-driven flows that match or exceed human performance.
- Build and run evaluations that measure whether agents complete tasks correctly, identify regressions, and uncover failure modes.
- Analyze production failures and systematically improve robustness.
- Partner with product to take agent flows from prototype through beta and general availability, defining quality standards.

### Agent platform and infrastructure
- Build the core agent harness, including memory systems, tool infrastructure, and retrieval architecture.
- Improve agent performance through prompting strategies, tool-use design, and context construction.
- Design guardrails and safety checks for predictable production behavior.
- Build a cross-surface evaluation framework for measuring quality, regressions, and performance.
- Create feedback loops that use production logs and real usage to improve prompts, tools, and evaluation coverage.
- Support teams building managed-agent variants for specific use cases.

## Requirements
- Experience building or shipping production systems with LLMs or agents, including prompting, tool-use design, agent orchestration, retrieval, structured extraction, or fine-tuning.
- Strong backend fundamentals in APIs, databases, and distributed systems.
- Experience designing model or agent evaluations, measuring regressions, or converting qualitative quality questions into measurable signals.
- Systems-and-outcomes mindset focused on user value and product reliability.
- Comfort debugging real-world failures and shipping iterative improvements quickly.

## Nice to have
- Experience with agent frameworks, tool-calling systems, or retrieval architectures such as vector search, hybrid search, and retrieval-augmented generation.
- Experience building or maintaining evaluation or benchmark infrastructure for LLM systems, or running fine-tuning in production.
- Experience with go-to-market, sales, or marketing workflows, including lead sourcing, enrichment, or audience building.
- Familiarity with React, TypeScript, Python, AWS, Aurora/Postgres, ECS/Fargate, Lambda, OpenSearch, ElastiCache/Redis, Terraform, and Datadog.
- Growth mindset and willingness to invest in team learning.

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