We are looking for an engineer to build and deploy an intelligent, autonomous operations agent that supervises our data pipeline infrastructure around the clock. The agent will predict delivery delays before they occur, verify data quality using adaptive statistical baselines, and synthesize code-level fixes for routine operational failures. You will own the agent end to end, from design and evaluation to production reliability.
We’re looking for experienced security engineers to evaluate how effectively static analysis tools detect vulnerabilities in GenAI applications. You’ll build small, runnable agent and RAG codebases containing realistic examples of Sensitive Information Disclosure and Excessive Agency, along with fixed and near-miss versions. You’ll then trace, annotate, test, and explain each finding. Main tasks listed: Build runnable agent/RAG repositories with code-reachable Sensitive Information Disclosure or Excessive Agency vulnerabilities across tool calling, memory, and MCP; Create vulnerable, fixed, and hard-negative variants with minimal security-relevant differences; Trace and annotate assets, data/action paths, controls, root causes, severity, and residual risk; Define authorization contexts and write deterministic tests validating vulnerable, fixed, and negative behavior.
We are looking for a Software Engineer with hands-on experience in full-stack development and some exposure to DevOps practices. You will contribute to building and operating applications that support critical engineering workflows. This role is ideal for someone who can independently deliver features, understands how systems run in production, and is looking to grow into owning services end-to-end. Main tasks listed: Design, develop, and maintain full-stack applications using React, TypeScript, and Python Build and enhance backend APIs and services for scalability and performance Collaborate with engineers and stakeholders to define and deliver technical solutions Participate in code reviews and contribute to improving engineering standards Debug and resolve issues across frontend, backend, and deployment environments Work with cloud platforms to deploy and operate applications Contribute to CI/CD pipelines and improve development and release workflows Help improve observability, monitoring, and reliability of applications.
We are looking for experienced Software Engineers with a strong background in developing and operating large-scale production systems. The ideal candidate has worked extensively on enterprise software, understands how production systems fail, has experience identifying security vulnerabilities in code, and has successfully implemented secure fixes and mitigations. Main tasks listed: Design and build realistic coding-agent benchmark tasks using production-like repositories, tests, configs, documentation, and runtime scenarios; Create benign engineering tasks such as bug fixes, feature additions, CI repairs, config migrations, integration updates, and runtime-state fixes; Define clear utility requirements to verify that the coding agent completes the requested task correctly; Define safety/alignment requirements to ensure the agent preserves system constraints, developer intent, data integrity, privacy, permissions, and oversight mechanisms.
We are seeking Engineering experts to contribute advanced technical expertise to data curation and evaluation for frontier AI models and labs. This senior reviewer role focuses on reviewing complex engineering problems, developing evaluation frameworks, and advising client teams assessing AI-generated technical outputs.
We are hiring US-based academic dermatologists to contribute senior clinical expertise to data curation for frontier AI models and labs. The work supports a longitudinal dermatology dataset used by AI developers, regulatory teams, and academic groups. This is the senior reviewer track of the project. You will lead clinical review on complex cases, contribute to the development of clinical evaluation frameworks, and serve as expert advisor for client teams reviewing AI model outputs. Main tasks listed: Lead senior-level clinical review on complex or ambiguous cases; Facilitate structured resolution of clinical disagreement among reviewers; Contribute to the development of clinical evaluation frameworks; Provide expert advisory hours to client teams reviewing AI model outputs. The listing says the work can involve sensitive or graphic content.
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.
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.
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.