Software Engineer

Mercor is hiring Software Engineers on behalf of a leading AI Lab building scalable systems to power the next generation of intelligent, autonomous agents. This role sits at the intersection of distributed systems engineering and agentic AI, focused on enabling advanced reasoning, multi-agent coordination, and real-world deployment at scale.

Responsibilities

• Design, build, and optimise distributed infrastructure for training, deploying, and scaling AI agents across high-performance compute environments.
• Develop core backend systems (services, APIs, and orchestration layers) that support agent lifecycles, tool execution, memory access, and multi-agent coordination.
• Collaborate closely with research and applied AI teams to integrate model-serving pipelines, agent reasoning loops, memory stores, and planning components into production systems.
• Build and maintain agent runtime infrastructure, including task scheduling, state management, inter-agent communication, and execution reliability.
• Implement monitoring, observability, and fault-tolerance mechanisms for long-running agent processes and distributed workflows.
• Evaluate and improve system performance across compute, networking, storage, and inference layers, identifying and resolving bottlenecks.
• Participate in synchronous collaboration sessions (4-hour windows, 2–3 times per week) to review architecture decisions, troubleshoot distributed systems, and iterate on design improvements.

Requirements

• Strong foundation in Computer Science, Software Engineering, or Systems Design, with experience building large-scale distributed systems.

• Proficiency in one or more backend or systems programming languages such as Go, Rust, Python, C++, Java, Scala, C#, Kotlin, or TypeScript/JavaScript.

• Experience with cloud infrastructure (AWS, GCP, or Azure) and containerisation/orchestration tools such as Docker and Kubernetes.

• Strong experience designing production-grade backend services, APIs, and distributed systems.

• Familiarity with LLM inference pipelines, agent frameworks, multi-agent architectures, or reinforcement learning environments is a strong plus.

• Knowledge of networking, data streaming, caching, and performance optimisation in distributed systems.

• Excellent collaboration and communication skills.

• Ability to commit 30-40 hours per week, including required synchronous collaboration sessions.

Why Join

• Work directly with a world-class AI research lab building the infrastructure behind next-generation agentic systems.
• Influence how intelligent agents are trained, deployed, and operated at scale in real-world environments.
• Operate at the frontier of software engineering and autonomous AI, contributing to foundational system design.
• Collaborate with leading systems engineers, AI researchers, and agent developers tackling hard problems in scalability, reliability, and autonomy.
• Join a global network of experts shaping the future of agent-driven AI infrastructure.