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.
We build the infrastructure that frontier AI labs use to evaluate coding agents and language models: benchmark datasets, agent evaluation runs at scale, and automated task quality checks. You would own features across the stack, from the data model and background jobs to the screens our users work in every day.
This is a role for an experienced engineer who works independently, makes sound tradeoffs, and turns a loosely defined request into something shipped, tested, and safe to leave alone.
What you will do- Design and ship features end to end: data model, API, background workers, and the UI when the change needs it.
- Build and improve how agents and models are evaluated: running trials in sandboxed environments, scoring results, and turning thousands of runs into results people can trust.
- Improve how benchmark tasks are quality-checked: verifiers, rubric-based and LLM-as-a-judge grading, and deciding whether a task is clear, solvable, and fair to score.
- Integrate new models, agents, and benchmarks in a way that generalizes, without a special case for each one.
- Keep the system fast and reliable as usage grows: efficient queries, safe migrations, and concurrency that holds up under load.
- Review teammates’ pull requests against our engineering guidelines and help keep the codebase consistent.
- Typically 5+ years building and operating production backend systems, with ownership of at least one service from design through on-call.
- Strong Python. Experience with Django (or a similar framework), PostgreSQL, and a task queue such as Celery. What matters most is having run a real system, not knowing a particular framework.
- Hands-on experience in LLM evaluation, beyond calling a model API. You understand how a benchmark task is built, how an agent trial is scored, and how results are aggregated and compared. Experience with Harbor, Terminal-Bench, SWE-bench, Inspect, or a similar harness is a strong plus.
- Judgment about task quality. You can spot ambiguous instructions, weak or gameable verifiers, leaked answers, and checks that pass for the wrong reason.
- Good tradeoffs. You reach for the smallest design that solves the problem, extend what already exists before adding something new, and can explain what you chose not to build.
- Ownership. You take a fuzzy request, find the relevant code, ask questions only when the answer changes the design, and finish the job, including migrations, tests, and edge cases users will hit.
- Quick adoption of team conventions. You read the written guidelines and your first pull requests already match them in naming, structure, tests, and error handling.
- Comfort with a TypeScript and React frontend when a change is visible there. You don’t need to be a frontend specialist.
- Clear writing. Your pull request descriptions and design notes explain why, not just what.
- Experience running agents in sandboxed or containerized environments (Docker, Kubernetes, or cloud sandboxes such as Modal or Daytona).
- Experience with LLM-as-a-judge or rubric-based grading, and with reducing judge bias and noise.
- Familiarity with coding agents such as Claude Code, Codex, or OpenHands, and with MCP.
- Experience with distributed job scheduling, concurrency limits, or database locking at scale.
- Contributions to open-source evaluation or infrastructure projects.
- Written engineering guidelines, applied consistently, with the same review bar for everyone.
- Business logic lives in services. Hot paths stay cheap. Lint and tests pass before a pull request is ready.
- The same vocabulary in the code, the API, and the UI.
- We prefer deleting code to adding it, and simple designs to clever ones.
A strong first month looks like several changes merged with light review, and one area of the system you can change confidently without a guide.
Interview process- A short conversation about a system you built and the tradeoffs you made.
- A practical exercise: a focused change in an existing codebase.
- A discussion of a flawed benchmark task: what’s wrong with the instruction, the verifier, and the scoring.
- A conversation with the team.
- 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.
- 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.
Skills Required
- Typically 5+ years building and operating production backend systems
- Ownership of at least one production service from design through on-call
- Strong Python experience
- Experience with Django or a similar framework
- Experience with PostgreSQL
- Experience with a task queue such as Celery
- Hands-on experience with LLM evaluation beyond calling model APIs
- Understanding of benchmark construction, agent trial scoring, and result aggregation
- Ability to assess task clarity, solvability, verifier quality, answer leakage, and scoring fairness
- Ability to design and ship end-to-end backend features
- Comfort with TypeScript and React frontend work
- Clear technical writing in pull requests and design notes
- Experience with Harbor, Terminal-Bench, SWE-bench, Inspect, or a similar evaluation harness
- Experience with sandboxed or containerized environments such as Docker, Kubernetes, Modal, or Daytona
- Experience with LLM-as-a-judge or rubric-based grading
- Familiarity with Claude Code, Codex, OpenHands, or MCP
- Experience with distributed job scheduling, concurrency limits, or database locking at scale
- Contributions to open-source evaluation or infrastructure projects
Turing Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Turing and has not been reviewed or approved by Turing.
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Fair & Transparent Compensation — Feedback suggests USD-denominated pay and access to higher-paying clients can outpace local benchmarks for many non‑U.S. developers. Payout timing and processing are described as predictable once engagements begin, which supports confidence in earnings.
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Wellbeing & Lifestyle Benefits — Feedback suggests remote‑first work with flexible hours is a consistent positive that enhances day‑to‑day balance. The ability to work from anywhere and maintain autonomy is frequently highlighted as part of the overall rewards experience.
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Healthcare Strength — Feedback suggests some U.S. corporate roles include comprehensive health benefits, with individual accounts referencing employer‑covered medical insurance. These signals indicate stronger healthcare support for certain employee populations.
Turing Insights
What We Do
We now live in a remote-first world and every company is in a race to find the best remote engineers. There are so many amazing engineers all over the world. Turing’s mission is to help unleash the world’s untapped human potential. More than 300 companies, including those backed by Google Ventures, Bloomberg, Andreessen, Founders Fund, and Kleiner are already using Turing to spin up their engineering dream teams. Turing’s hiring platform combines the planetary reach and AI to deliver your ideal engineers in order to help you spin up your engineering dream team. Our deep matching intelligence finds the best Turing developers across 100+ skills like React, Node, Python, Golang, Angular, Swift, Java, and many more. As part of our rigorous vetting process, we also review software engineers’ technical abilities, English skills, and remote working capabilities. Turing ensures time zone overlap, transparency, and reliable communication in order to make remote development easy for you after the match. The Turing team has deep expertise in AI and building engineering dream teams in the U.S. at top companies. Turing company is backed by well-known investors like Facebook’s initial CTO (Adam D’Angelo), executives from Google, Facebook, Amazon, Twitter, Founders Fund (investors in Facebook, Tesla, Asana, etc). Turing.com is led by serial A.I. entrepreneurs Jonathan Siddharth and Vijay Krishnan, their last A.I. firm leveraged remote talent and had a successful acquisition.
