Software Engineer, Agents
AI summary of the role
Mercor is hiring a Software Engineer to build agentic products that scale, working across backend, frontend, data, and orchestration stacks.
What you’ll do
- Own agentic features end-to-end — from scoping with researchers/ops partners through implementation, launch, and iteration on real customer feedback.
- Design and ship LLM agents, harnesses, and verifiers — including the tools, prompts, and policies that make them reliable.
- Build the Python/FastAPI services and Temporal/Modal pipelines that orchestrate agent runs, human-in-the-loop review and iterations.
- Build state of the art RL environments that expand the capabilities of frontier agents, with realistic enterprise apps, simulated coworkers, and rich company data rooms.
What you’ll bring
- Strong engineer with experience building agentic products that scale.
- Proficiency in Python, FastAPI, Django, Pydantic (backend).
- Proficiency in Next.js, React, TypeScript, Tailwind (frontend).
- Experience with PostgreSQL, MySQL, Snowflake, DuckDB, Redis (data).
Technologies
Python · FastAPI · Django · Pydantic · Next.js · React · TypeScript · Tailwind · PostgreSQL · MySQL · Snowflake · DuckDB
About Mercor
Marketplace matching domain experts (doctors, lawyers, scientists, engineers) with AI labs to generate post-training data, evaluations, and reinforcement learning signal.
Series C
Source and classification
Internal deployment & tooling · Evidence for this classification:
valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. About the Role We're looking for a strong engineer who can build agentic products that scale. You will work with: Backend: Python, FastAPI, Django, Pydantic Frontend: Next.js, React, TypeScript, Tailwind Data: PostgreSQL, MySQL, Snowflake, DuckDB, Redis Orchestration/Infra: Kubernetes, Temporal, Modal, Woz Agents/LLM: LangGraph, LangChain, FastMCP, Harbor, NemoGym Observability: Datadog, PostHog, LangSmith At the end of the process, you’ll be team-matched to where you can have the most impact, on one of the following: Automation – We build intelligent systems and agents that automate operational work at scale—handling talent management, decision-making insights, and knowledge access—so humans can focus on higher-level thinking.This is a newly formed, CEO-facing team focused
More from the job description
About Mercor Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. About the Role We're looking for a strong engineer who can build agentic products that scale. You will work with: Backend: Python, FastAPI, Django, Pydantic Frontend: Next.js, React, TypeScript, Tailwind Data: PostgreSQL, MySQL, Snowflake, DuckDB, Redis Orchestration/Infra: Kubernetes, Temporal, Modal, Woz Agents/LLM: LangGraph, LangChain, FastMCP, Harbor, NemoGym Observability: Datado [... source excerpt omitted ...] tion of next-generation models. What You’ll Do Own agentic features end-to-end — from scoping with researchers/ops partners through implementation, launch, and iteration on real customer feedback. Design and ship LLM agents, harnesses, and verifiers — including the tools, prompts, and policies that make them reliable. Build the Python/FastAPI services and Temporal/Modal pipelines that orchestrate agent runs, human-in-the-loop review and iterations. Build state of the art RL environments that expand the capabilities of frontier agents, with realistic enterprise apps, simulated coworkers, and rich company data rooms that support tasks spanning hours to days. Build tooling tha
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