Software Engineer / Research Engineer, Agent Orchestration

Location: San Francisco, CA or New York, NY
Company Stage of Funding: Series C AI Company ($231M+ Raised)
Office Type: Onsite (5 Days Per Week)
Salary: $200,000–$400,000 + Competitive Equity

Company Description

We're representing one of the fastest-growing AI infrastructure companies building the orchestration layer that powers enterprise-grade AI agents. Their platform enables leading consumer and enterprise brands to deploy conversational AI agents across voice, chat, email, and messaging channels, handling millions of customer interactions at scale.

Backed by top-tier investors and experiencing hypergrowth, the company is building the systems that determine how AI agents reason, select tools, execute workflows, and continuously improve in production. As part of the Agent Orchestration team, you'll work on some of the most challenging engineering problems at the intersection of distributed systems, applied machine learning, experimentation, and agent infrastructure.

What You Will Do

• Design and build the orchestration systems that power AI agents in production environments.
• Develop agent harnesses responsible for workflow execution, model routing, tool orchestration, and safety controls.
• Build distributed backend systems that support real-time and long-running agent workflows.
• Design control-plane infrastructure for planning, execution, routing, and model selection.
• Improve latency, reliability, scalability, and production correctness across agent runtime systems.
• Analyze real-world failure modes and build feedback loops that continuously improve agent performance.
• Develop experimentation frameworks, A/B testing infrastructure, and evaluation systems.
• Improve observability, monitoring, simulation, and testing capabilities across the platform.
• Collaborate closely with Research, Infrastructure, and Product teams to push the frontier of agent behavior and performance.
• Contribute to voice infrastructure, transcription pipelines, and other latency-sensitive systems.

Ideal Background

• 3–15 years of software engineering experience.
• Strong background building distributed systems, backend platforms, or large-scale infrastructure.
• Experience operating in highly ambiguous, fast-moving, and technically challenging environments.
• Proven track record of owning systems from design through production deployment and iteration.
• Experience working on frontier or greenfield technical problems rather than incremental product improvements.
• Strong understanding of system reliability, observability, debugging, and production operations.
• Experience working near the machine learning layer, including applied ML systems, recommendation systems, ranking systems, perception systems, or agent infrastructure.
• Excellent problem-solving ability and strong engineering fundamentals.

Preferred

• Experience building agent orchestration frameworks, execution engines, agent runtimes, or agent harnesses.
• Applied machine learning engineering experience in production environments.
• Background at high-growth startups, quantitative trading firms, or frontier technology companies.
• Experience with experimentation systems, evaluation frameworks, or feedback-driven optimization loops.
• Strong Computer Science, Machine Learning, Mathematics, or related technical education.
• Advanced degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a quantitative field.
• Experience working with real-time systems, voice infrastructure, or latency-sensitive distributed applications.
• Familiarity with multi-model orchestration, tool-calling systems, planning frameworks, or AI agent architectures.

Compensation and Benefits

• Base salary: $200,000–$400,000.
• Competitive equity package.
• Opportunity to work on cutting-edge AI agent infrastructure used by major enterprise customers.
• Join a rapidly scaling company backed by leading venture investors.
• Exposure to some of the most advanced problems in agent orchestration, experimentation, and AI systems reliability.
• High-impact environment with strong technical ownership and autonomy.
• Collaborative, in-person engineering culture focused on speed, execution, and innovation.
• Multiple career growth paths across engineering leadership, infrastructure, applied AI, and research-oriented initiatives.