This role involves analyzing large-scale security telemetry, threat intelligence feeds, and detection data to uncover patterns and support strategic initiatives. You will work cross-functionally with product and engineering teams to transform raw data into actionable insights, design dashboards, and maintain data pipelines.
This role involves architecting and designing security services with global system integrators and telco partners, leveraging CrowdStrike's Falcon platform. Responsibilities include driving platform adoption, developing technical plans, and building relationships with partner teams across various security domains.
This role involves driving adoption of NVIDIA's AI and computing platforms among independent software vendors (ISVs) by providing technical guidance, enabling integrations, and fostering co-innovation. The person will work closely with developer teams and ISV partners to optimize solutions using NVIDIA technologies like CUDA and Triton, while translating field feedback into product roadmap insights. Key responsibilities include leading technical workshops, developing enablement resources, and collaborating across engineering, product, and marketing to scale developer engagement in emerging tech domains.
Develop and optimize compilers for AI inference workloads on next-generation NVIDIA GPUs, focusing on kernel generation, computational graph optimizations, and hardware-software co-design. Collaborate with cross-functional teams to advance AI performance and scale across datacenters. Work at the forefront of compiler innovation for deep learning and AI architectures.
This role involves deploying and scaling machine learning models for high-fidelity engineering simulations, working directly with customers to integrate AI into production environments. The Principal ML Engineer will lead technical architecture, build robust data and inference pipelines using Python, PyTorch, and Kubeflow, and mentor teams across global customer sites. The position bridges advanced physics-based challenges with practical AI solutions in industries like aerospace, energy, and semiconductors.
This role involves building and operating scalable ML infrastructure for training and serving large physics models, with a focus on distributed training optimization, data pipeline performance, and model deployment. You'll work closely with research scientists and ML engineers in a high-impact R&D environment, solving systems-level challenges in GPU clusters and HPC workflows. The position emphasizes end-to-end ownership of research infrastructure, reproducibility, and developer experience for fast iteration.
As a Machine Learning Engineer, you will build and deploy production-grade ML systems, collaborate with cross-functional teams to solve critical client challenges, and lead technical scoping and architectural decisions. You will work on high-stakes, high-impact missions in national security and AI safety, ensuring AI is secure, trustworthy, and safe for all.
This role involves working closely with ElevenLabs' most strategic customers to design and implement high-impact technical solutions. Responsibilities include full ownership of major projects, collaborating with customer teams, and driving innovation through coding and problem-solving.
This role involves building and managing datasets and evaluation systems for AI safety, translating safety policies into actionable data criteria, and creating scalable processes for data collection, labelling, and model assessment. The Data Scientist will work across policy, research, and engineering to ensure models are evaluated using real-world data and robust quality standards. A strong focus is placed on developing reproducible pipelines and operationalising safety frameworks in a fast-moving AI environment.
This role involves prototyping and developing innovative cybersecurity solutions using AI, with a focus on enhancing Darktrace’s platform to detect and respond to unknown threats. You’ll work in a cross-functional R&D team, balancing creative exploration with structured development of scalable features. The position emphasizes ownership of novel projects, collaboration, and advancing core capabilities in Python and containerized environments.
This role involves developing and evaluating AI safety mechanisms for a generative AI system that conducts clinical interviews to assess the risk of psychosis. The researcher will work with a multidisciplinary team to address safety risks, develop LLM supervisor modules, and conduct empirical evaluations using simulation and human ratings.
This is an open application for Faculty's technical talent pool, inviting professionals in engineering, data science, and DevOps to express interest for future roles. The company focuses on delivering human-centric, responsible AI solutions for high-impact clients across government, finance, and other critical sectors. Work is project-based, collaborative, and driven by intellectual curiosity and technical excellence, with a strong emphasis on diversity and positive societal impact.
This role involves leading FPGA and system-on-chip design for advanced gimbal payload systems, including video processing, sensor interfaces, and real-time control. You will work closely with software, control, and mechanical engineering teams to develop in-house solutions from prototype to production.
This role involves leading project management for a multi-year osteoporosis drug discovery programme within a TechBio environment. You'll coordinate across interdisciplinary teams including wet lab biology, machine learning, and data science, translating scientific objectives into integrated delivery plans. The position requires managing cross-team dependencies, evolving workstreams, and governance processes in a discovery-stage R&D setting with high ambiguity and scientific complexity.
Develop and scale generative and predictive machine learning models for cellular behaviour using multi-omic biological data. Build robust distributed training and inference systems, bridging ML research and production deployment in close collaboration with scientists. Optimize performance across compute, memory, and I/O while advancing core ML infrastructure in a highly interdisciplinary environment.
