This role involves designing, implementing, and maintaining large-scale HPC/AI clusters, managing job/workload schedules, and developing CI/CD pipelines. You will work closely with HPC, OS, GPU compute, and systems specialists to architect and optimize performance platforms, and support R&D activities.
Design and develop novel Transformer and GNN-based foundation models that generalize across relational databases and heterogeneous graphs. Work across the full ML lifecycle—from architecture design to inference optimization—on real-world applications like fraud detection, forecasting, and recommendation systems. Collaborate with researchers and engineers to build a unified AI model for enterprise data.
Lead a distributed engineering team building critical connectivity infrastructure for AI agents, focusing on secure, scalable access to external systems via the MCP standard. Responsibilities include managing token management, OAuth flows, and proxy systems that power tool use across Anthropic's products. The role emphasizes operational rigor, cross-team alignment, and technical leadership in a high-trust, enterprise-focused environment.
Lead the development of internal review and enforcement tooling used to investigate potential harms in AI systems, both for first-party products and third-party platforms. Build a scalable, privacy-preserving platform with analytics, sandbox environments, and human-in-the-loop automation, enabling safety investigators and AI (Claude) to collaborate effectively. Partner with policy, legal, and data science teams to ensure systems are trustworthy, compliant, and aligned with evolving safety standards.
About the CompanyOur client is a boutique investment firm at the forefront of quantitative investing, combining advanced machine learning, artificial intelligence, and fundamental research to support investment decision-making. The firm has built a sophisticated in-house data and analytics platform that...
As a Quantitative Researcher, you will develop and test innovative signals, models, and trading strategies for global financial markets. You'll work closely with a small, high-performing team to design and implement research tools and data pipelines, applying rigorous statistical and computational methods.
This role involves serving as the senior technical advisor for education-sector clients, guiding them from AI exploration to production adoption. The Applied AI Architect designs secure, scalable solutions using OpenAI’s models, aligns technical strategies with institutional goals, and leads cross-functional teams to deliver measurable impact in teaching, research, and operations. The role requires deep technical expertise, stakeholder engagement across academic and administrative domains, and a strong commitment to responsible AI deployment.
This role involves designing functional proteins using proprietary generative AI models, bridging machine learning and experimental validation teams. The scientist will iteratively optimize protein designs based on lab feedback, contribute to AI model improvement, and help refine data workflows. The position emphasizes interdisciplinary collaboration, scientific rigor, and innovation in synthetic biology.
This role is part of an open talent pool for future opportunities at Relation Therapeutics, a tech-driven biotech company focused on drug discovery using single-cell multi-omics and machine learning. The company seeks candidates in computational and discovery life sciences, as well as ML engineering with high-performance GPU computing experience. Positions will support interdisciplinary research and development in London’s state-of-the-art labs, contributing to transformative therapies for diseases like fibrosis, osteoarthritis, and osteoporosis.
Design and implement production-grade machine learning and AI workloads on the Databricks platform, with a focus on GenAI, MLOps, and LLMOps. Provide technical leadership during sales cycles by building MVPs, guiding architecture, and mentoring teams. Influence product direction and drive AI adoption through technical content and industry engagement.
Design and implement production-grade machine learning and AI workloads on the Databricks platform, with a focus on GenAI, MLOps, and LLMOps. Lead technical engagements with enterprise customers, build end-to-end AI pipelines, and provide expert guidance on RAG architectures, agentic systems, and AI observability. Mentor internal teams and influence product direction by representing customer needs in AI roadmap discussions.
About the TeamThe Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI's mission to build and...
Develop and scale Unreal Engine 5 applications to generate high-quality synthetic imagery for training and validating AI and computer vision models. Collaborate with AI researchers, 3D artists, and engineers to automate complex scene generation workflows involving lighting, weather, and camera systems. Transform research prototypes into robust, production-grade software with CI/CD integration and support deployment in secure environments.
This role focuses on designing and implementing evaluation systems to measure the real-world performance of AI agents within Cohere’s North platform. You'll translate customer needs, product direction, and usage data into meaningful metrics that guide model development, ensuring improvements align with enterprise workflows like research, document editing, and automation. The position bridges product and modelling teams, requiring deep expertise in applied machine learning evaluation and a strong focus on measurement rigor.
This role involves developing generative and predictive models of cellular behaviour using single-cell multi-omics and perturbation data, with a focus on causal inference, counterfactual prediction, and guiding experimental design. The scientist will work iteratively with wet-lab teams to validate hypotheses and refine models, operating at the intersection of machine learning and experimental biology. The position emphasizes mechanistic fidelity, real-world utility, and interdisciplinary collaboration in a high-impact drug discovery environment.
