C++ AI Jobs

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark the AI/ML/DL models, and publish the models, data sets, and evaluations; deploy models in production by containerizing the models; work with customers and internal employees to refine the quality of the models; establish continuous learning pipelines for models with online learning or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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Lead the design and execution of the AI’s robotics research agenda, recruit, mentor, and manage a small team of research scientists and engineers in the London lab, collaborate with the world model and simulation teams to develop state-of-the-art training platforms for robotics, guide the creation of persistent 3D/4D scene representations and advanced embodied AI methodologies, drive research efforts in scene understanding, sim-to-real transfer, and advanced planning, foster partnerships with leading ML researchers, hardware specialists, and external collaborators, and help establish the lab's technical culture and external reputation.

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Collaborate with the world model team to build a state-of-the-art training and simulation platform for robotics. Develop persistent 3D/4D scene representations that maintain temporal consistency. Unlock advanced robotics planning and decision making through in-house, cutting-edge world models. Ensure sensing and system dynamics perform reliably in high-stakes, real-world operations where models think, simulate, and act. Partner with ML researchers to innovate on generative models and physical AGI. Collaborate closely with ML researchers developing multimodal world models and generative systems, and with hardware teams and partners to ensure robotic platforms, sensing, and system dynamics behave optimally in real-world operation.

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Contribute to the technical development and integration of advanced robotic automation solutions for manufacturing automation, utilizing the Intrinsic platform, ROS and state-of-the-art AI capabilities. Collaborate closely with research and industry partners to successfully integrate AI and automation capabilities into factory settings. Document designs, processes, and results, communicating effectively with internal technical teams and partners.

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The Lead Member of Technical Staff, Inference Infrastructure, is responsible for providing technical leadership across multiple teams, driving the architecture and strategy for deploying optimized NLP models to production in low latency, high throughput, and high availability environments. They lead the design of customized deployments to meet specific customer needs and mentor engineers to raise the technical standards across the team. The role involves contributing to the development, deployment, and operation of the AI platform delivering large language models through easy-to-use API endpoints, and serving as a key point of contact for customers.

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The role involves contributing to the development of next-generation, high-performance machine learning serving systems. Responsibilities include building infrastructure that powers AI applications, working on problems at the intersection of distributed systems, machine learning, and high-performance computing, and solving fundamental computer science problems impacting AI deployment. Specific projects include implementing asynchronous inference for non-blocking client requests, designing intelligent request routing systems to balance load across thousands of model replicas with strict latency SLAs, building traffic management systems for zero-downtime model updates handling terabytes of inference requests, improving state management for scale from thousands to tens of thousands of replicas, architecting frameworks for multi-model orchestration in complex ML pipelines ensuring end-to-end latency guarantees, and developing observability and debugging tools for distributed ML applications at scale. The work involves writing performance-critical code in Python (with Cython optimizations) and potentially C++, working with distributed systems at scale using Ray Core's actor system, gRPC, and custom networking protocols, extending cloud-native infrastructure such as Kubernetes and service meshes, gaining system-level knowledge of ML/AI frameworks like TensorFlow, PyTorch, JAX, and transformers, and ensuring production reliability with tools like OpenTelemetry, Prometheus, distributed tracing, and chaos engineering to maintain 99.99% uptime. The role also involves leveraging AI coding agents to enhance team productivity while maintaining high code quality standards.

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Build and refine performance models that translate microbenchmark results into cost-to-serve estimates. Analyze inference workloads end to end across applications, models, and fleet infrastructure. Enhance tooling to identify bottlenecks across layers for latency and throughput. Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference.

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As a Staff Software Engineer on the Perception team, you will be responsible for defining and driving the long-term vision and architecture for perception systems, architecting complex, scalable, and robust end-to-end perception and robotics systems for deployment on real-world hardware, ensuring their successful integration into Hayden’s core product platform. You will spearhead the architectural design, implementation, and long-term ownership of next-generation perception systems, transition research prototypes to production solutions, deliver high-performance, tested, and maintainable C++ code optimized for edge and robotics platforms, architect and optimize real-time perception pipelines, drive the integration of state-of-the-art ML and CV models, provide technical leadership in complex problem domains, collaborate with Product leadership and Engineering organizations, and contribute to foundational shared infrastructure, tooling, and architectural patterns to scale pilot initiatives into core product capabilities.

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As a Robotics Application Engineer specializing in Intelligent Manufacturing Automation, the responsibilities include closely collaborating with the automation group and industry partners to advance manufacturing automation by extending capabilities and solutions. Daily tasks involve integrating and developing robotics solutions, including processes, software features, and integrating state-of-the-art AI for manufacturing automation. The role includes working with the team leading the deployment of impactful robotic systems in production. Additional responsibilities entail contributing to the technical development and integration of advanced robotic automation solutions for manufacturing automation using the Intrinsic platform, ROS, and AI capabilities, collaborating with research and industry partners to integrate AI and automation into factory settings, and documenting designs, processes, and results while communicating effectively with internal technical teams and partners.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This involves understanding the AI systems built at Helsing, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.