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Turing

Engineering Manager

Turing is hiring an Engineering Manager. Level rates it ; you can apply on Turing.

Level's read on this job

  • Works on AI ●●●○ is how Level rates this role: the daily work is on AI products, without building the model.
  • The posting mentions LangChain, AI agents and fine tuning among its tools and skills.
  • Turing has 46 other open roles on Level.
  • Posted in the last few days.

AI in this role

LangChain
ai-agentsfine-tuningai-evaluationai-research
About Turing

Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com.

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.

Values

  • We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.

  • We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection

  • We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.

Advantages of joining Turing

  • Work at the frontier of AI, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.

  • Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.

  • Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges.

  • Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies.

  • Move at the pace of AI innovation, with the speed, ownership, and impact of a startup.

Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

For applicants from the European Union, please review Turing's GDPR notice here.

How we rate this

Engineering Manager at Turing rates 67 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsFine-tuningAI EvaluationAI ResearchLangChain

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. How do you decide that one model's output is better than another's for a given task?
  4. Tell me about a research question you investigated. What did you find?
  5. Walk me through how you've used LangChain in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: AI agents, Fine-tuning, AI Evaluation, AI Research, and LangChain. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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