Embedded software engineer h1.location_city

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 .