Director of Engineering, Internal Platforms

What You'll Do9

• Set the architecture and growth plans for internal platforms that support operational workflows, expert management, data collection, analytics, and business processes.
• Add LLMs, AI agents, and automation to internal tools to cut manual work and improve operations.
• Design APIs, distributed services, event-driven systems, and integrations for onboarding, assessments, subscriptions, data pipelines, and financial workflows.
• Lead quality engineering across automated tests, CI/CD quality gates, release checks, and production reliability.
• Expand automated regression tests and apply AI-assisted testing and evaluation methods to conventional and AI-powered applications.
• Take part in technical design and implementation, review code, diagnose production problems, and resolve complex engineering issues.
• Introduce AI-assisted development practices, developer tools, and automation while maintaining engineering standards.
• Mentor engineers, set technical standards, and guide teams toward ownership, experimentation, and accountability.
• Work with Product, AI/ML, Data, and Operations to turn business needs into scalable technical systems.

Requirements9

• At least 10 years in software engineering, including 4 or more years in engineering leadership.
• Recent hands-on experience building and debugging production software in Python, TypeScript, or a similar language.
• Strong background in platform architecture, including distributed systems, microservices, APIs, event-driven designs, and workflow orchestration.
• Practical experience with AI systems, including LLM integrations, agentic workflows, RAG, and evaluation frameworks.
• Experience using AI development tools and automation to improve engineering productivity and delivery.
• Quality engineering experience with automated testing, CI/CD pipelines, API and end-to-end tests, and production reliability.
• Cloud infrastructure experience with AWS, GCP, or Azure, along with Kubernetes, databases, observability, and DevOps practices.
• Experience building internal platforms for operational workflows, data pipelines, analytics, or complex business processes.
• A record of technical leadership that includes mentoring engineers, making architecture decisions, and delivering work amid ambiguity.

Preferred Qualifications6

• Background in AI research labs, AI-native companies, or high-growth technology startups.
• Experience with human data platforms, such as expert management, data annotation, human-in-the-loop workflows, data curation, or AI evaluation pipelines.
• Familiarity with LLM training and evaluation, including human feedback, preference data, and model benchmarking.
• Experience with agent orchestration frameworks such as LangGraph or Temporal.
• Experience building internal developer platforms, shared infrastructure, or self-service engineering tools.
• Familiarity with AI-assisted testing, automated test generation, or evaluation of nondeterministic systems.

Who Should Apply

The ideal candidate brings 10+ years in software engineering and at least 4 years leading engineers, with recent production coding experience. Strong platform architecture, quality engineering, and practical AI systems experience will prepare you to guide internal tools from design through production.

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