Language: English

We are seeking Mathematics experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science. You will design tasks involving numerical analysis, optimization, statistics, mathematical modeling, probability, dynamical systems, and computational mathematics. The role focuses on evaluating whether AI agents can formulate mathematical problems, implement reliable algorithms, operate in a terminal environment, debug numerical workflows, and produce verifiable computational results. Main tasks listed: Design and build authentic computational mathematics tasks — translate mathematical and research workflows into self-contained, multi-step terminal environments, complete with datasets, equations, model definitions, constraints, and expected outputs; Implement expert solutions using Python, R, Julia, C/C++, Bash, or other relevant tools, covering areas such as optimization, numerical integration, differential equations, matrix computation, statistical inference, stochastic modeling, or algorithm analysis; Define rigorous grading criteria, including numerical accuracy, convergence, complexity, feasibility, and mathematical correctness, along with appropriate tolerances, stopping criteria, stability requirements, and reproducibility controls; Develop automated tests and debug issues — validate results across edge cases and alternative valid implementations, and troubleshoot problems involving floating-point precision, solver failures, conditioning, convergence, and performance.

We are seeking Subject Matter Experts (SMEs) with expertise in psychology, social work, counseling, qualitative research, HCI, communication, conflict resolution, coaching, or related advisory fields to support AI conversation evaluation research. SMEs will review conversations between participants and AI models, assess whether sufficient context was gathered before recommendations were made, identify missed opportunities for clarification, and provide structured feedback to improve conversational quality. Main tasks listed: Review AI conversations involving real-world interpersonal and everyday decision-making scenarios; Identify missing context and recommend relevant follow-up questions; Evaluate whether additional information would have changed the AI’s recommendations; Distinguish between critical (“load-bearing”) and non-essential information.

As a Task Executor – ERP / Business Systems, you will operate ERPNext on the Frappe Framework to complete assigned business workflows accurately. You will create, submit, cancel, and verify documents such as quotations, sales orders, purchase orders, invoices, journal entries, stock entries, BOMs, and work orders across Accounting, Stock, Buying, Selling, Manufacturing, Assets, Projects, and HR. Main tasks listed: Execute assigned workflows in ERPNext using the provided instructions and values; Create, submit, cancel, and verify quotations, orders, invoices, journal entries, stock entries, BOMs, and work orders; Navigate Accounting, Stock, Buying, Selling, Manufacturing, Assets, Projects, and HR modules; Capture requested document IDs, reports, ledger effects, stock updates, screenshots, and exported files. This work may involve recording or sharing personal data (for example voice, video or account activity): read the consent and privacy terms on the official page before applying.

This role involves creating, reviewing, and improving tasks that reflect the practical work performed by lawyers. The individual will analyze legal issues, apply laws and principles, assess the accuracy of responses, and refine outputs for clarity and usefulness. Strong legal knowledge, judgment, research skills, and attention to detail are essential. Main tasks listed: Produce high-difficulty expert tasks and gold-standard solutions in the functional area; Review and calibrate team members’ output and own day-to-day quality; Escalate genuine edge cases and rubric ambiguities to the Functional SME (Expert) Lead; Education & Experience.

Design, build, and optimize multi-step agentic workflows capable of multi-hop reasoning, hypothesis generation, and technical validation. Collaborate closely with mathematical researchers to translate complex, abstract proofs into structured technical specifications and automated pipelines. Main tasks listed: Design, build, and optimize multi-step agentic workflows capable of multi-hop reasoning, hypothesis generation, and technical validation; Collaborate closely with mathematical researchers to translate complex, abstract proofs into structured technical specifications and automated pipelines; Develop reusable architectural frameworks, tooling, and memory systems for STEM-focused AI agents; Evaluate and iterate on agent performance, ensuring high accuracy, reliability, and trace logic across execution steps.

As a Task Executor – Aerodynamics, you will use Flow5 v7 to complete assigned aerodynamic analysis workflows involving foils, wings, aircraft, hydrofoils, and sails. You will receive the required geometry files, project files, operating conditions, and execution steps, and you will follow them exactly. Your work may include modifying geometries, configuring analyses, generating conforming meshes, running Type 1 or Type 2 polars, and exporting the requested results. Main tasks listed: Open and inspect foil, plane, mesh, and project files in Flow5; Build or modify foils, wings, planes, hydrofoils, and other assigned geometries; Configure operating conditions, polar settings, geometry parameters, and mesh requirements; Generate conforming meshes and run Type 1 or Type 2 polar analyses. This work may involve recording or sharing personal data (for example voice, video or account activity): read the consent and privacy terms on the official page before applying.

As a Task Executor – Bioinformatics, you will use UGENE to complete assigned sequence-analysis and molecular-biology workflows. You will receive the required files, values, and step-by-step instructions, and you will execute them exactly as provided. Tasks may include sequence alignment, phylogenetic tree construction, plasmid annotation, workflow execution, and result export. You will save the requested outputs, record your screen when required, and report any ambiguity or mismatch without guessing or improvising. Main tasks listed: Open provided sequence files, plasmid maps, alignments, and tree files in UGENE; Perform instructed operations such as MUSCLE or ClustalW alignments, tree building, plasmid annotation, and workflow execution; Inspect sequence data, annotations, alignments, and analysis results for task-specific verification; Export aligned FASTA files, annotated GenBank files, reports, screenshots, and other requested outputs. This work may involve recording or sharing personal data (for example voice, video or account activity): read the consent and privacy terms on the official page before applying.

We are seeking Life Sciences Subject Matter Experts to develop advanced scientific reasoning tasks for LifeSciBench. Unlike coding-focused benchmarks, this role emphasizes deep biological knowledge, scientific interpretation, and rigorous reasoning. You will create challenging questions based on realistic scientific scenarios, experimental results, biological data, figures, literature-style evidence, and research problems. This is a non-code benchmark: tasks are not executed, and no programming is required. Main tasks listed: Design novel, graduate-level life sciences problems grounded in realistic research contexts; Develop clear questions and rigorous reference answers with defensible scientific reasoning; Interpret experiments, datasets, figures, sequences, structures, and other biological evidence; Validate biological claims, calculations, assumptions, and conclusions.

Urgent hire: ASAP start, 4 week contract. Applications reviewed on a rolling basis, apply early. Main tasks listed: Use AI coding agents throughout development, QA, and validation; Participate in onboarding, calibration, and quality reviews; deliver to the quality bar on agreed milestones.

We are seeking a Dataset Enablement Manager to bridge the technical gap between scientific data pipelines and centralized commercial teams. In this role, you will package complex technical datasets into high-impact collateral, educate client-facing teams on domain-specific benchmarks, and drive the multi-client sell-through of our OTS dataset portfolio to leading global AI labs. Main tasks listed: Pipeline Tracking & Visibility: Maintain real-time oversight of active dataset pipelines across all frontier science, math, and scientific software engineering teams; Technical Collateral & Sample Packaging: Assemble production-grade sample packs, schema definitions, benchmark comparisons, and technical documentation to effectively showcase dataset quality; Commercial Enablement & Training: Lead regular deep-dives and training sessions for sales, account, and solutions teams to communicate value propositions, methodology, and target LLM use cases; Sell-Through Performance & Monetization: Track multi-client utilization metrics, identify under-monetized dataset assets, and drive performance reporting to maximize commercial sell-through.