Jobs at Turing

Build and support production enterprise applications for asset management clients. Own full-stack features from requirements and technical design through testing, deployment, and production support. Develop Python backend services, React interfaces, APIs, integrations, and database layers while maintaining security, quality, documentation, and reliability. Use AI-assisted development tools responsibly, investigate production issues, collaborate with stakeholders, and exercise independent technical judgment. Experience with AI agents, financial technology, and enterprise software is advantageous.

Build and operate infrastructure for evaluating coding agents and language models. Own end-to-end features across data models, APIs, background workers, and user interfaces; run sandboxed agent trials; develop scoring and task-quality systems; integrate models and benchmarks; optimize performance, concurrency, migrations, and reliability; and review code. The role requires independent ownership of production backend systems and hands-on LLM evaluation experience.

Supports strategic and operational initiatives through research, data analysis, project planning, performance tracking, process improvement, and leadership reporting. The role coordinates cross-functional teams, develops insights and dashboards, monitors milestones, and helps turn ambiguous business challenges into structured plans and actionable outcomes. It offers exposure to strategy, operations, product, engineering, data science, and go-to-market functions in a fully remote environment.

Own and improve multi-cloud production infrastructure across GCP, AWS, and Azure. Build developer platforms, CI/CD pipelines, deployment automation, Terraform modules, and production Kubernetes environments. Lead incident response, root-cause remediation, observability, networking, security, cost optimization, and reliability initiatives. The role requires hands-on automation and programming, collaboration with security and application teams, mentoring, global on-call participation, and regular overlap with U.S. Pacific Time.

Own end-to-end delivery of software-engineering data programs, including scope, timelines, quality, throughput, cost, contributor performance, and customer reporting. Coordinate hundreds of distributed engineers, optimize annotation and evaluation workflows, analyze quality data, build automation and reusable playbooks, and serve as the primary client contact for AI research organizations. The role requires technical fluency sufficient to inspect code, tests, workflows, and evaluation results while leading large-scale operational execution.