Software Engineer (Staff), Platform Engineering

What You'll Do9

• Set the technical direction for platform design, development, and scaling across operational workflows, data pipelines, expert lifecycle management, and intelligence delivery.
• Design distributed services, APIs, event-driven architectures, and integrations that handle complex workflows, high-volume data processing, and cross-platform communication.
• Build infrastructure for expert-generated data, enterprise AI datasets, human feedback, and multimodal data workflows used in model training and evaluation.
• Architect solutions for pipeline creation, talent onboarding, subscription management, task execution, and automated operational processes.
• Develop services that aggregate, process, and surface data, metrics, and insights from multiple systems into unified, actionable experiences.
• Create reusable infrastructure for AI agents, covering tool calling, orchestration, context management, structured outputs, evaluation, observability, and reliable execution.
• Write production-grade code, lead technical design reviews, troubleshoot complex issues, and set engineering standards across critical systems.
• Apply AI-assisted development, automated testing, CI/CD, and developer tooling to speed delivery and raise software quality.
• Mentor engineers, guide architectural decisions, reduce technical debt, and partner with Product, AI/ML, Data, and Operations to deliver scalable solutions.

Requirements10

• 10+ years of software engineering experience, with a strong record of designing, building, and scaling complex production systems.
• Deep hands-on programming expertise in Python, TypeScript, or similar modern languages, plus strong backend engineering fundamentals.
• Extensive experience with distributed systems, microservices, APIs, event-driven architectures, asynchronous processing, and workflow orchestration.
• Strong system design and architecture skills, including the ability to make technical trade-offs across scalability, reliability, performance, and maintainability.
• Experience building data-intensive platforms, including data ingestion, processing pipelines, ETL/ELT workflows, and analytics infrastructure.
• Practical experience with LLMs and agentic systems, including tool calling, agent orchestration, RAG, context management, and evaluation frameworks.
• Experience with cloud infrastructure such as AWS, GCP, or Azure, containerization, Kubernetes, databases, and CI/CD pipelines.
• Strong understanding of engineering quality, including automated testing, observability, performance optimization, and production reliability.
• Proven technical leadership, with experience guiding engineers, driving cross-team initiatives, and owning complex technical projects from design through production.
• High ownership and execution mindset, with the ability to navigate ambiguity, solve difficult technical problems, and deliver in fast-paced environments.

Preferred Qualifications6

• Experience in AI research labs, AI-native companies, or high-growth technology startups.
• Experience with human data infrastructure, including expert-generated datasets, data annotation, human-in-the-loop workflows, preference data, and model evaluation pipelines.
• Familiarity with foundation model training and evaluation workflows, including multimodal datasets, robotics data, and human demonstrations.
• Experience building enterprise AI data platforms, retrieval systems, or infrastructure supporting AI agents.
• Hands-on experience with agentic frameworks and orchestration tools such as LangGraph, Temporal, MCP, or similar technologies.
• Experience designing real-time analytics, operational intelligence, and data aggregation systems across multiple platforms.

Who Should Apply

Engineers with 10+ years of experience who have built and scaled complex production systems and led cross-team technical initiatives. You should bring hands-on Python or TypeScript skill, deep knowledge of distributed systems and data pipelines, and practical work with LLMs and agentic systems. The role suits someone who takes ownership, navigates ambiguity, and mentors other engineers.

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