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 on 0→1 product development, with a strong emphasis on business impact. The work is highly cross-functional, touching nearly every system across the company
Studio – We own Mercor’s evaluation system & annotation platform for RL environments and tasks. We build harnesses, agents, verifiers, and the end-to-end infrastructure for producing frontier data. Our mission is to scale up high quality RL environments/tasks and expand their capabilities. We work closely with researchers at frontier AI labs to jointly shape the direction of next-generation models
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 that turns agent trajectories into insight, from statistical analysis to automated failure mode detection
Build and refine the full-stack surfaces and data infrastructure — craft Next.js/React interfaces where operators and experts work with agents, evolve data models to give agents the structured context and audit trails they need
Define agent quality and drive continuous improvement — build evals, instrument traces, analyze failure modes, and iterate on prompts, tools, and guardrails while raising the bar for reliability, cost, latency, and UX
Partner cross-functionally to shape agent autonomy — work with Product, Design, Research and Ops to draw the lines between autonomous action, propose-and-approve flows, and human-in-the-loop decisions