Company: Turing

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.

We are seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This is a non-coding role focused on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning. You will create challenging, objectively defensible problems based on realistic research scenarios, experimental results, biological data, figures, sequences, structures, and literature-style evidence. 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.

We are developing a computer usage dataset to train intelligent systems that understand how users interact with software applications. The project involves recording your screen while performing detailed, multi-step tasks — such as configuring applications, exploring tools, or completing productivity workflows. Each task may include 100+ individual interactions (clicks, keypresses, and other on-screen events). Main tasks listed: Record your screen activity while completing assigned computer-based tasks; Carefully follow provided instructions and documentation to complete each task step-by-step; Ensure recordings are complete, high-quality, and interruption-free; Label or verify events (clicks, keystrokes, actions) when required. 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 Earth Sciences experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science. You will create computational workflows involving environmental data, climate systems, atmospheric processes, geophysics, oceanography, geology, and related fields. You will design tasks that require AI agents to inspect scientific datasets, process geospatial or time-series data, run models, troubleshoot pipelines, and generate objectively verifiable scientific outputs. Main tasks listed: Translate authentic Earth-science workflows into self-contained terminal benchmark tasks; Prepare geospatial, climate, atmospheric, geological, hydrological, or oceanographic datasets; Build reproducible computational environments with appropriate scientific libraries and command-line tools; Create expert solutions using Python, R, Bash, Julia, or domain-specific software. The listing says the work can involve sensitive or graphic content.