Posted:1 day ago|
Platform:
Remote
Contractual
Our Client is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems.
Client helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.
Powering this growth is Client talent cloud—an AI-vetted pool of 4M+ software engineers, data scientists, and STEM experts who can train models and build AI applications. All of this is orchestrated by ALAN—our AI-powered platform for matching and managing talent, and generating high-quality human and synthetic data to improve model performance. ALAN also accelerates workflows for model and agent evals, supervised fine-tuning, reinforcement learning, reinforcement learning with human feedback, preference-pair generation, benchmarking, data capture for pre-training, post-training, and building AI applications.
Client—based in San Francisco, California—was named #1 on The Information's annual list of "Top 50 Most Promising B2B Companies," and has been profiled by Fast Company, TechCrunch, Reuters, Semafor, VentureBeat, Entrepreneur, CNBC, Forbes, and many others. Client leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, X, Stanford, Caltech, and MIT.
Analyze and triage GitHub issues across trending open-source libraries.
Set up and configure code repositories, including Dockerization and environment setup.
Evaluating unit test coverage and quality.
Modify and run codebases locally to assess LLM performance in bug-fixing scenarios.
Collaborate with researchers to design and identify repositories and issues that are challenging for LLMs.
Opportunities to lead a team of junior engineers to collaborate on projects.
Minimum 5+ years of overall experience
Strong experience with at least one of the following languages: Ruby
Proficiency with Git, Docker, and basic software pipeline setup.
Ability to understand and navigate complex codebases.
Comfortable running, modifying, and testing real-world projects locally.
Experience contributing to or evaluating open-source projects is a plus.
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