Full Stack Engineer

About Company:

Our client is a Palo Alto–based AI infrastructure and talent platform founded in 2018. It helps companies connect with remote software developers using AI-powered vetting and matching technology. Originally branded as the “Intelligent Talent Cloud,”enabled companies to “spin up their engineering dream team in the cloud” by sourcing and managing vetted global talent.

In recent years, they have evolved to support AI infrastructure and AGI workflows, offering services in model training, fine-tuning, and deployment—powered by their internal AI platform, ALAN, and backed by a vast talent network. They reported $300 million in revenue and reached profitability. Their growth is driven by demand for annotated training data from AI labs, including major clients like OpenAI, Google, Anthropic, and Meta.

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Role Overview :

We’re a coding-focused team at Turing that serves as a research partner for a Frontier AI Lab. Our role is to build coding tasks, evaluations, datasets, and tooling that help train and improve large language models (LLMs). You’ll write and debug production-quality code, design rigorous evaluations, and build reproducible workflows that generate clean, high-signal data for model training. Attention to detail matters deeply here—small mistakes can cascade into misleading results, so precision and thoroughness are essential. You’ll also collaborate closely with engineers, researchers, and quality owners to align on standards, review work, and continuously raise the quality bar.

If you enjoy solving unusual technical problems, investigating subtle model failures, and working in developer-like environments where correctness, reproducibility, and collaboration matter, this role will keep you very entertained.

What does your day-to-day look like :

• Write, review, and debug code across multiple languages
• Design tasks and evaluation scenarios for coding, reasoning, and debugging
• Investigate LLM outputs and identify hallucinations, regressions, and failure modes
• Build reproducible dev environments using Docker + automation tools
• Develop scripts, pipelines, and tools for data generation, scoring, and validation
• Produce structured annotations, judgments, and high-quality datasets
• Run systematic evaluations that help improve model reliability and reasoning

Required Skills :

• Experience using LLM coding tools (Cursor, Copilot, CodeWhisperer)
• Strong hands-on coding experience (professional or research-based) in one or more of: Python, JavaScript / Node.js, TypeScript
• (Additional languages like Go, Java, C++, C#, Rust, SQL, R, Dart, etc. are a plus)
• Solid experience with Linux + Bash, scripting, and automation
• Strong with Docker, reproducible environments, and dev containers
• Advanced Git skills (branching, diffs, patches, conflict resolution)
• Solid understanding of testing and QA (unit, integration, negative, edge-case focused)

Nice-to-Haves:

• Experience using LLM coding tools (Cursor, Copilot, CodeWhisperer)
• Experience with dataset creation, annotation, evaluation, or ML pipelines
• Familiarity with benchmarks like SWE Bench or Terminal Bench
• Background in QA automation, DevOps, ML systems, or data engineering

Who Thrives Here:

• Engineers who enjoy breaking things and understanding why
• People who like designing tasks, running experiments, and debugging
• Detail-oriented folks who can spot subtle issues in code or model behavior
• Engineers who like building clean, reusable workflows rather than one-off hacks

Preferred Background:

• Bachelor’s degree in a technical field with 6+ years experience
• Master’s degree in a technical field with 4+ years experience
• PhD in a technical field with 2+ years experience