As a Senior AI Engineer at PoliceAI, you will lead the design, development, and deployment of AI-enabled products, translating policing needs into technical solutions. You will work with multidisciplinary teams to build secure, maintainable, and scalable services, and support the testing, evaluation, and validation of AI products.
As Head of AI, you will lead the development and implementation of AI strategies, governance, and capabilities at LPPA, a leading pension administration company. You will work closely with the CTO and executive team to identify and deliver AI opportunities that enhance operational efficiency, service quality, and member outcomes. Key responsibilities include building the AI team, establishing governance frameworks, and fostering AI adoption across the organization.
Design and optimize high-performance deep learning inference software for NVIDIA's GPU-accelerated platforms, focusing on frameworks like vLLM and SGLang. Improve model serving efficiency for large language and generative AI models across datacenter and edge devices. Collaborate with research and engineering teams to implement cutting-edge algorithms and performance optimizations using CUDA, Triton, and other low-level tools.
This role involves designing, optimizing, and maintaining high-performance deep learning inference software for large-scale language and generative AI models. The engineer will work on GPU-accelerated frameworks like vLLM, SGLang, and FlashInfer, implementing cutting-edge algorithms and performance improvements across NVIDIA's full range of accelerators. Collaboration with open-source communities and cross-team partners is central to advancing model serving efficiency and deployment at scale.
Lead the development and deployment of deep learning models to enhance underwriting systems, focusing on non-traditional and multi-modal data. Work closely with stakeholders to translate R&D into production solutions, using advanced neural networks like LSTMs, RNNs, and Transformers. Operate in a collaborative, innovation-driven environment with strong support for professional growth and technical excellence.
Lead the development and deployment of deep learning models to enhance underwriting systems using non-traditional and multi-modal data. Design and train neural networks such as LSTMs, RNNs, and Transformers, bridging research with production deployment. Collaborate with stakeholders to drive data science initiatives from prototype to impact, while mentoring junior team members and shaping technical direction.
A Machine Learning Engineer will collaborate with simulation engineers, data scientists, and customers to solve complex physics and engineering challenges using AI. The role involves building scalable, reliable ML data pipelines, working with 3D point-cloud and mesh data, and translating R&D into reusable tools and products. Frequent customer site visits across multiple continents are expected to support on-site solution development and deployment.
Develop and deploy scalable machine learning systems integrated with high-fidelity physics simulations, working directly with customers and engineering teams to solve real-world industrial challenges. Focus on building reliable data pipelines, manipulating 3D point-cloud and mesh data, and translating research into production-grade tools. The role involves significant customer collaboration, on-site problem solving, and travel across multiple regions.
The role involves translating research into commercial solutions, developing physics-informed models for ship performance, and integrating these into NeuralShipper, a platform for automated vessel design. The engineer will work on multidisciplinary optimization and wind-assisted propulsion systems, collaborating with industry stakeholders.
This role involves designing and prototyping generative machine learning models for protein design, with a strong focus on real-world biological applications. The scientist will build and evaluate models, collaborate across interdisciplinary teams, and iterate based on wet lab feedback. A significant emphasis is placed on high-performance code, scalable data pipelines, and staying current with advances in ML and synthetic biology.
This role involves collaborating on high-impact machine learning projects, evaluating and optimizing PyTorch models, and contributing to AI research publications. Fellows work remotely on flexible schedules, engaging with cutting-edge generative AI systems and interdisciplinary experts. The position emphasizes research, model interpretation, and advancing responsible, human-centered AI through technical evaluation and publication.
This role involves prototyping, building, and implementing new AI approaches, as well as evaluating their quality and efficiency. You'll work in a dynamic R&D team at a leading AI company, focusing on data-driven solutions to AI problems.
This role involves designing and implementing machine learning solutions to enhance decision-making and operational performance in a growing manufacturing supply business. The candidate will work with complex datasets, lead data science initiatives, and introduce new ways of working to a traditionally operated company.
This role involves designing, developing, and deploying secure AI and ML solutions for autonomous and mission-critical systems. Responsibilities include building machine learning models, supporting autonomous capabilities with ROS 2, and ensuring adherence to Responsible AI and safety standards.
Design and implement machine learning models tailored to single-cell and multiomic biological data to understand cellular responses to interventions. Collaborate closely with wet-lab scientists to translate biological questions into modelling problems and ensure models capture biologically meaningful insights. Contribute to research roadmap development and advance cellular modelling through interdisciplinary collaboration.
Develop and optimize end-to-end AI driver models for autonomous vehicles, focusing on training, evaluation, and deployment across real-world and synthetic data environments. Work closely with production and validation teams to deliver high-impact driving behaviors for L2 to L4 systems, with ownership from data curation to on-road testing. Engage in fast iteration cycles to improve model performance in safety-critical automotive applications.
Build and scale performance tooling for AI systems across cloud and embedded hardware, focusing on profiling, prediction, and optimization of deep learning models. Analyze bottlenecks across the full AI stack—from models to accelerators—and deliver data-driven insights to guide performance decisions. Collaborate with cross-functional teams to establish standards and drive efficiency in model training and inference.
