Top Tech Jobs & Startup Jobs in NYC, NY

Conduct research on frontier AI systems, including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarking, and evaluation. Design rigorous experiments, datasets, prototypes, tooling, and research workflows; train and assess models; analyze results; and translate findings into scalable AI products. Collaborate across research, engineering, product, and operations teams, communicate technical conclusions, contribute to publications or open-source work, and mentor engineers and researchers.

Leads large-scale AI and software-engineering data programs, owning delivery scope, timelines, quality, throughput, contributor performance, and cost. Designs workflows, quality-control systems, metrics, and capacity plans for coding datasets, agentic trajectories, benchmarks, and evaluations. Manages distributed contributor and reviewer networks, partners with talent teams, communicates with AI-lab customers, analyzes operational data, and converts successful processes into reusable playbooks and evaluation assets.

Opportunity for software developers, founders, engineering teams, and code owners to submit existing repositories for licensing evaluation. Eligible code includes abandoned startups, personal projects, internal tools, SaaS products, and discontinued applications. Applicants must own or legally control the code and exclude unauthorized third-party material. Compensation is based on agreed licensing terms, with reference rates for non-exclusive licensing.

Build realistic backend coding challenges, feature implementations, bug fixes, and deterministic verifiers for AI training projects. Evaluate code correctness, reliability, maintainability, and performance while incorporating automated testing, validation criteria, edge-case analysis, and secure coding practices. Work independently on scalable backend systems and APIs, refine tasks based on requirements, and document technical solutions clearly.

Debug and improve software across diverse codebases, identify and remediate vulnerabilities, conduct security audits and penetration testing, refactor legacy systems, optimize backend applications, and document technical findings. The role contributes cybersecurity and software engineering expertise to AI training datasets while working independently on project-defined tasks.

Research Fellows will validate AI-generated paper reproductions, simulations, and results; map research taxonomies; define standards for high-quality AI-assisted science; and create benchmark tasks and evaluations for frontier AI models. Fellows will contribute to a publicly released benchmark paper and open evaluation set as lead or first authors. The contract offers flexible, remote global work for current STEM PhD students or postdocs with published research and hands-on AI agent experience.