Future Tech Jobs
Future Tech Jobs
Design and experiment with novel Transformer and GNN architectures to build a unified foundation model that understands relational databases and heterogeneous graphs. Work across the full ML lifecycle—from architecture design to inference acceleration—enabling applications in forecasting, fraud detection, and recommendation systems. Collaborate with researchers and engineers to push the boundaries of relational reasoning in enterprise AI.
This role involves designing and experimenting with novel Transformer and Graph Neural Network (GNN) architectures to build a unified foundation model that understands relational databases and heterogeneous graphs. The engineer will work across the full ML lifecycle—from architecture design to inference acceleration—enabling applications in fraud detection, forecasting, and recommendation systems. Collaboration with researchers and engineers is central, with a focus on pushing beyond traditional single-table AI models.
This role involves working closely with prospective and existing customers to ensure a seamless integration of Luminance’s AI technology. Responsibilities include managing Proof of Values, providing expert advice, and designing custom workflows to meet specific client needs. The role offers the opportunity to build relationships with a diverse range of companies and support the sales process.
Engage with senior decision-makers through cold calling and targeted prospecting to generate new business for Luminance’s AI-powered contract platform. Drive lead pipeline growth by securing meetings and event attendance, using social selling and collaboration with sales and marketing teams. Work in a fast-paced, results-driven environment focused on high-performance and measurable outcomes.
Provide frontline technical support to customers by diagnosing and resolving straightforward issues, triaging more complex cases, and ensuring timely, clear communication throughout. Work closely with internal teams to escalate and track incidents, maintaining accurate records and contributing to process improvements. The role emphasizes responsiveness, customer focus, and efficient issue management within a fast-growing AI technology environment.
This role involves designing and leading the technical architecture for a safety-critical Remote Assistance system that connects autonomous vehicles with human operators in real time. The focus is on low-latency data streaming, fallback mechanisms, and system reliability to support Wayve’s Robotaxi launch. The position requires deep expertise in real-time systems, cross-team leadership, and translating technical decisions into operational safety and product outcomes.
Lead a growing engineering team building the real-time runtime platform for an AI-driven autonomous driving system. Focus on system design, middleware, performance optimization, and observability to enable reliable, low-latency operation across diverse vehicle platforms. Collaborate closely with software, hardware, and OEM teams to scale the AI Driver to new embodiments.
Optimise machine learning inference for edge accelerators and GPUs, focusing on efficient execution of large transformer models on low-power devices. Work across compilers, runtimes, kernels, and embedded deployment to deliver high-performance, reliable in-vehicle computing systems. Collaborate with model developers and contribute to technical roadmaps in an early-stage, high-impact environment.
Lead a team building offline scene understanding models to evaluate and improve Wayve's autonomous driving systems. Develop robust, scalable models that leverage high-compute environments to generate counterfactual insights, assess driver behaviour, and inform model development. Work across UK and US teams to unify technical direction and accelerate the AV development loop through rigorous ML engineering and cross-functional collaboration.
Lead a team of data scientists who deliver experimental and observational analysis to accelerate the development of Wayve’s end-to-end AI for autonomous vehicles. Drive evals and insights from real and simulated driving data, enabling engineering teams to rapidly iterate and meet customer commitments. Scale impact through people, tooling, and automation in a fast-paced, innovative environment.
Design and develop embedded software, SDKs, and hardware abstraction layers to enable autonomous driving systems to run consistently across diverse automotive platforms. Integrate core AV functions like inference, control, and localisation while minimising platform-specific customisation. Work across Linux and QNX systems, collaborating with hardware partners and cross-functional teams to ensure robust, scalable, and maintainable embedded solutions.
As a Product Feature Owner for ADAS, you will take end-to-end responsibility for specific ADAS functionalities, ensuring they meet OEM requirements and align with internal capabilities. You will collaborate closely with OEMs, product teams, and engineering teams to define feature scope, set performance targets, and ensure compliance with industry standards.
This role involves advancing embodied AI for autonomous driving through research in world modeling, spatial intelligence, and scalable decision-making. You'll develop foundation models, reinforcement learning systems, and sim-to-real transfer techniques in a multidisciplinary environment. The work spans simulation, robotics, and real-world deployment, with a focus on long-horizon prediction and cross-embodiment learning.
This role involves designing and maintaining cloud infrastructure, CI/CD pipelines, and developer tooling on Azure using Terraform. The engineer will own IAM and RBAC systems for a multi-tenant platform, support Kubernetes deployments, and build internal tools to improve developer productivity and platform security. The position emphasizes hands-on engineering with a focus on scalability, safety, and developer experience within a high-autonomy team.
This role involves developing and enhancing Wayve’s simulation platform for autonomous driving, focusing on high-fidelity robot emulation, visual realism, or scalable cloud infrastructure. The engineer will integrate machine-learned graphics and robotic systems into production-grade Python systems, working closely with research, robotics, and data teams. The position emphasizes technical leadership in building accurate, scalable simulations that support AI-driven vehicle development.
