Software Engineer, AI Platform

Mixpanel is a leader in analytics, and they are seeking a driven Software Engineer to join their AI Platform team. The role involves building scalable, secure, and reliable infrastructure to accelerate AI agent development and empower AI development across the company.

Responsibilities:

• Platform Architecture: Design and develop the core backend services, APIs, and microservices that enable product teams to easily and securely leverage AI models
• Agent Orchestration: Build scalable frameworks and tools to support multi-step agent workflows, including task decomposition, tool invocation, and persistent memory
• Evaluation & Reliability: Build robust evaluation systems to continuously measure reasoning quality, hallucination rates, and task success
• Operational Excellence: Architect high-performance serving infrastructure with strict guarantees around latency, throughput, cost-efficiency, and error handling
• Observability & MLOps: Ensure comprehensive monitoring, structured logging, and distributed tracing across all deployed AI models
• Collaboration: Partner with designers, product managers, and other engineers to build self-serve infrastructure that transforms our AI development cycle
• Leadership: Advocate for software engineering best practices, conduct thorough design and code reviews, and mentor junior engineers

Requirements:

• Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience
• 2+ years of professional software engineering experience
• Strong full-stack fundamentals: you're comfortable working across frontend, backend, and data layers
• Excellent debugging and technical investigation skills
• Strong technical communication, ideally with experience collaborating in an asynchronous remote environment
• Ability to move fast and iterate in ambiguous environments: you take ownership and focus on delivering value to users
• Hands on experience integrating and orchestrating LLMs and agents
• Experience building AI native systems, including iteratively improving and scaling them in production
• Familiarity with techniques used to optimize AI agents: eval frameworks, agent tooling, vector search engines, context engineering, and prompt engineering
• A desire to be on the forefront of leveraging AI to drive product improvement, observability and product analytics at scale