Jobs at Gramian Consulting Group

Design and validate reproducible, multi-step computational life sciences workflows for evaluating AI agents. Create datasets, instructions, computational tasks, expert solutions, ground truths, and grading criteria. Execute and troubleshoot scientific analyses, verify code and results, validate assumptions and calculations, and ensure workflows run reliably in isolated environments.

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

Leads end-to-end delivery of large-scale AI and software-engineering data programs, managing scope, timelines, quality, throughput, contributors, and costs. Designs workflows for coding datasets, agentic trajectories, reinforcement-learning environments, benchmarks, and evaluations. Builds quality-control systems, analyzes operational metrics, manages distributed technical contributors, communicates with AI lab customers, translates research goals into task specifications, automates reporting with code, and develops reusable operational playbooks.

Opportunity for software developers, founders, engineering teams, and code owners to submit existing repositories for licensing evaluation. Eligible codebases may include abandoned startups, side projects, internal tools, SaaS products, and discontinued applications. Applicants must own or legally control licensing rights and exclude unauthorized third-party material. Compensation is negotiated through licensing transactions, with reference rates for non-exclusive licenses based on source-code and Git-history token counts.

Develop realistic backend coding challenges, including feature implementations and bug fixes, for AI training projects. Design deterministic verifiers, automated tests, and validation criteria that distinguish correct from incorrect solutions. Evaluate code for correctness, reliability, maintainability, security, and performance while identifying edge cases and bottlenecks. Work independently with backend technologies and refine tasks based on project requirements and quality standards.

Debug and improve diverse codebases by fixing software defects, identifying vulnerabilities, conducting security assessments, refactoring legacy code, and optimizing secure backend systems. The specialist will document technical findings and contribute cybersecurity and software engineering expertise to AI training datasets. Work is fully remote and requires independent execution across project specifications.