12 days ago
São Paulo, BrazilSenior
Responsibilities
- Own services end to end, including design, implementation, rollout, and production operation.
- Build backend services and data pipelines that generate, validate, score, and version reproducible datasets.
- Develop high-performance internal interfaces with virtualized tables, server-side filtering, review workflows, and annotation UIs.
- Build and operate agent harnesses with multi-step tool-use loops, retries, and structured outputs against repositories and test suites.
- Create sandboxed execution environments for safe, deterministic, high-volume execution of model-generated code, along with RL environments and evaluation harnesses.
- Trace and debug agent runs end to end, including stalled loops, failed tool calls, and grader disagreements.
- Own infrastructure as code and CI/CD, debug production issues across the stack, and drive reliability improvements.
- Contribute through code reviews and design documents while partnering with researchers and quality owners to define evaluation quality.
Requirements
- 6+ years of experience building and operating production software, ideally at product companies operating at scale.
- Deep Python experience with production FastAPI and/or Django, ORM performance, migrations, and asynchronous patterns.
- Strong PostgreSQL experience including schema design, query optimization, and indexing, plus NoSQL experience.
- Production frontend experience with TypeScript, React, and Next.js.
- Fluency with Linux and Docker, plus Terraform or comparable infrastructure as code on AWS or GCP and ownership of CI/CD delivery.
- Daily use of LLM coding tools such as Claude Code, Cursor, or Copilot, with the ability to critically review their output.
- Experience building dependable systems on top of LLM APIs, such as an agent, pipeline, or evaluation system.
- Comfort working with non-deterministic systems and using controlled experiments to distinguish regressions from noise.
- Advanced Git skills and strong testing discipline covering unit, integration, negative, and edge cases.
- Clear written communication and the ability to work independently and challenge incorrect specifications.
- Bachelor's or master's degree in Computer Science, Software Engineering, Data Science, Machine Learning, AI, or a programming-heavy IT field, with demonstrated outcomes accepted in place of a matching degree.
- Preferred experience includes internal tools or developer platforms, agent frameworks and protocols, sandboxing and untrusted code execution, workflow orchestration, Kubernetes, LLM coding benchmarks, evaluation frameworks, or RLHF/RLVR.
Benefits
- Work at the frontier of AI by building datasets, reinforcement-learning environments, and benchmarks for leading AI labs.
- Contribute to leading-edge AI research and potentially showcase work at conferences such as ICLR, ICML, and NeurIPS.
- Apply frontier AI innovations to enterprise business challenges.
- Collaborate with colleagues who have experience at Google, Meta, Amazon, and other leading technology companies.
- Work with startup-level speed, ownership, and impact.
Tech Stack
Apache AirflowAWSDjangoDockerFastAPIGitGoogle Cloud PlatformKubernetesLinuxMongoDBNext.jsPostgreSQLPythonReactTerraformTypeScript
Categories
About Turing
Turing builds AI data pipelines and systems for enterprises and AI labs. It creates large-scale datasets and reinforcement-learning environments to train and evaluate models, and deploys agentic AI applications in domains like software engineering and enterprise workflows. The company also operates an AI-powered talent cloud to match and manage developers and AI trainers. Founded in 2018 and headquartered in San Francisco, it is privately held and serves Fortune 500 customers.