Engineering Manager

Role Overview

We are seeking an Engineering Manager to lead the development of internal tooling and quality-control systems that power AI training workflows. This role is ideal for a leader who enjoys working across application development, infrastructure, and applied AI. You will be part of a rapidly growing team solving complex problems in AI systems, working in a startup-paced environment with high expectations and short timelines.

The ideal candidate is someone who takes ownership beyond what their role technically requires, has a track record of shipping production systems, and is excited by the intensity of a fast-paced, ambiguous environment rather than looking for stable, predictable work.

Key Responsibilities

Tooling and Platform Development:

• Build scalable platforms supporting data creation, annotation, evaluation, review, and delivery.
• Design and implement APIs, internal applications, and developer tooling that enable efficient workflows.
• Develop maintainable, well-tested code that scales with growing demands.

Quality and Efficiency:

• Improve dataset quality, task validation, model evaluation, and production efficiency.
• Build and iterate on evaluation frameworks that ensure quality and reliability of AI training outputs.
• Collaborate with research and domain experts to translate requirements into practical solutions.

Architecture and Standards:

• Own architecture, technical delivery, reliability, security, and engineering standards.
• Design and implement data pipelines for dataset creation, annotation, and delivery at scale.
• Contribute to technical design and architecture decisions that balance speed, quality, and maintainability.

Leadership and Mentorship:

• Mentor engineers and establish strong design, testing, and release practices.
• Partner with Operations, Product, and AI specialists to identify bottlenecks and deliver pragmatic solutions.
• Lead teams through urgent, high-pressure delivery schedules while maintaining quality and engineering excellence.

Technical Skills

Web Development - Node.js, React, NestJS, Next.js, Express

Python Engineering and Data Science - FastAPI, NumPy, Jupyter notebooks. Proficiency in large-scale data generation and data pipeline orchestration. Frameworks like Spark, Beam, or Dataflow preferred. Statistical analysis, data visualization, and data manipulation.

Infrastructure - Kubernetes, Docker, Pulumi, Terraform, infrastructure-as-code

AI Engineering - LangChain, LiteLLM, RL Gyms, LoRA and fine-tuning, AI agents, and Harbor

Quality Systems - Dataset validation, evaluation frameworks, automated testing, and QC tooling

Qualifications

• Strong software engineering fundamentals with a proven track record of shipping production systems.
• Experience leading teams effectively in fast-paced, ambiguous environments.
• Demonstrated ability to balance speed, quality, scalability, and maintainability in technical decisions.
• Strong debugging, problem-solving, and communication skills with cross-functional teams.
• Interest in the evolving challenges of training and evaluating AI systems.

What Success Looks Like

You lead a team that ships high-quality solutions enabling AI training workflows. You can identify technical bottlenecks, design pragmatic solutions, and guide your team to implement them reliably. You're energized by working across the full stack from infrastructure to application code, and you're excited about the technical challenges of building evaluation and quality-control systems for AI at scale while developing strong engineers.