Engineering Manager

Turing

17h

About The Position

Turing is seeking an Engineering Manager to lead development of internal tooling and quality-control systems that power AI training workflows. The role spans application development, infrastructure, and applied AI, and involves working on complex AI systems in a fast-paced startup environment with high expectations and short timelines. The ideal candidate takes ownership beyond formal role requirements, has shipped production systems, and is energized by ambiguity and intensity. The manager will lead a team building scalable platforms and data pipelines for data creation, annotation, evaluation, review, and delivery. They will own architecture and technical delivery, reliability, security, and engineering standards; improve dataset quality and production efficiency; and partner with research, operations, product, and AI specialists to translate requirements into practical solutions. They will also mentor engineers and guide teams through urgent delivery schedules while maintaining engineering quality. Success means leading a team that reliably ships high-quality systems for AI training workflows, identifying bottlenecks and developing pragmatic solutions, and building evaluation and quality-control systems for AI at scale while developing strong engineers.

Requirements

Strong software engineering fundamentals and 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.

Nice To Haves

Experience with data pipeline frameworks such as Spark, Beam, or Dataflow.

Responsibilities

Build scalable platforms supporting data creation, annotation, evaluation, review, and delivery.
Design and implement APIs, internal applications, and developer tooling to enable efficient workflows.
Develop maintainable, well-tested code that scales with growing demands.
Improve dataset quality, task validation, model evaluation, and production efficiency.
Build and iterate on evaluation frameworks to ensure the quality and reliability of AI training outputs.
Collaborate with research and domain experts to translate requirements into practical solutions.
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.
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.