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À propos de ce poste Director of Engineering, Internal Platforms chez micro1

micro1 · Télétravail · Remote — 58 countries

Job Title: Director of Engineering, Internal Platforms


Job Type: Full-time


Location: Remote


The Role

We’re looking for a highly technical, hands-on Director of Engineering to lead the architecture, development, and scalability of the internal platforms powering our operations, data workflows, and business ecosystem.


You’ll own the engineering direction of mission-critical systems while overseeing quality engineering, ensuring our platforms are reliable, scalable, and built for automation.


This role requires a strong technical leader who stays close to the code, drives architectural decisions, and leverages AI-powered tools and modern engineering practices to accelerate development. You’ll lead engineering teams, establish technical standards, and deliver systems that simplify complex operations at scale.


What You’ll Be Doing

  1. Lead Internal Platform Engineering: Own the architecture, development, and scalability of platforms supporting operational workflows, expert lifecycle management, data collection, analytics, and business processes.
  2. Build Intelligent Systems: Integrate LLMs, AI agents, and automation into internal platforms to streamline workflows, reduce manual effort, and improve operational efficiency.
  3. Drive Platform Architecture: Design scalable APIs, distributed services, event-driven systems, and integrations connecting onboarding, assessments, subscriptions, data pipelines, and financial workflows.
  4. Own Quality Engineering: Lead QE strategy, automated testing frameworks, CI/CD quality gates, release validation, and production reliability across platforms.
  5. Advance Testing Automation: Implement AI-assisted testing, automated regression coverage, and evaluation frameworks for traditional and AI-powered applications.
  6. Stay Hands-On: Drive technical design, contribute to implementation, review code, troubleshoot production issues, and resolve complex engineering challenges.
  7. Improve Engineering Productivity: Introduce AI-assisted development workflows, developer tooling, and automation to accelerate delivery while maintaining engineering quality.
  8. Lead Engineering Teams: Mentor engineers, establish technical standards, drive execution, and foster a culture of ownership, experimentation, and accountability.
  9. Partner Cross-Functionally: Work with Product, AI/ML, Data, and Operations to translate business needs into scalable technical solutions.


What We’re Looking For

  1. 10+ years of software engineering experience, including 4+ years in engineering leadership roles.
  2. Strong hands-on development expertise in Python, TypeScript, or similar languages, with recent experience building and debugging production systems.
  3. Deep experience in platform architecture, including distributed systems, microservices, APIs, event-driven architectures, and workflow orchestration.
  4. Practical experience with AI-powered systems, including LLM integrations, agentic workflows, RAG, and evaluation frameworks.
  5. Experience leveraging AI development tools and automation to improve engineering productivity and delivery.
  6. Strong Quality Engineering expertise, including automated testing, CI/CD pipelines, API and end-to-end testing, and production reliability.
  7. Cloud infrastructure proficiency with AWS, GCP, or Azure, Kubernetes, databases, observability, and DevOps practices.
  8. Experience building internal platforms supporting operational workflows, data pipelines, analytics, or complex business processes.
  9. Proven technical leadership, with the ability to mentor engineers, make architectural decisions, and deliver in fast-paced, ambiguous environments.


Preferred Qualifications

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


Compensation & Benefits Notice

The national pay range for this full-time position is base salary of $280,000 –$350,000. All employees are eligible for equity compensation, and employees may also receive performance-based bonuses, dependent on role and subject to company policies. micro1 provides a comprehensive benefits package, including up to 100% reimbursement for health-insurance premiums, paid time off, a 401(K) plan with a company match, and additional benefits designed to support a high-performing, remote-first workforce.


micro1 is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation during the application process, reach out to support@micro1.ai.


Our hiring process utilizes artificial intelligence tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

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À propos de micro1

micro1 is a human data and talent infrastructure company focused on improving AI systems through high-quality expert input.


It connects vetted domain specialists with AI teams and enterprises to produce training data, run model evaluations, and improve reasoning performance in advanced AI systems.


The platform combines a talent marketplace with AI-driven screening and matching tools, enabling faster access to specialized contributors for tasks like LLM training, agent evaluation, and structured data creation.

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