Lead ML Product Engineer

About the role

About our team: Radiant is redefining how AI infrastructure is built. We design and operate AI-native infrastructure platforms engineered for sovereignty, performance, and scale — powering GPU-native workloads, multi-tenant control planes, and high-performance AI systems for the most demanding environments. We are building purpose-built AI infrastructure from powered land, to compute, to software. As we scale our operations and deploy capital into the next generation of AI infrastructure, we are looking to expand our finance team with leaders who can combine technical strength with execution excellence and are driven to build. Radiant was established by Brookfield, a leading global alternative asset manager with over US$1 trillion of assets under management across real estate, infrastructure, renewable power and transition, private equity and credit. Brookfield's global relationships, investment expertise and access to long-term institutional capital provide Radiant with a differentiated platform from which to develop, finance and operate AI infrastructure assets. This combination of entrepreneurial execution and institutional sponsorship enables Radiant to pursue large-scale GPU and AI infrastructure opportunities globally. Job Summary: As an ML Product Engineer, you will work at the intersection of product, machine learning, and cloud infrastructure, helping turn Radiant’s ML platform capabilities into usable, high-quality customer experiences. You’ll work across GPU workloads, training, inference, APIs, SDKs, developer tooling, and platform integrations, partnering closely with product, engineering, infrastructure, and customers. This is a hands-on technical role focused on rapidly prototyping, validating, and shipping ML platform capabilities that make it easier for customers to build and run AI workloads on Radiant’s platform.   Key Responsibilities: Build and prototype ML platform features, workflows, SDKs, APIs, and developer tooling. Work closely with Product to turn customer needs into working technical solutions and product experiences. Develop reference implementations across training, fine-tuning, inference, model serving, and orchestration. Integrate ML frameworks and tools with Radiant’s GPU, Kubernetes, storage, networking, and platform services. Work directly with customers and internal teams to identify friction and improve developer experience and time-to-value. Evaluate emerging ML infrastructure technologies and rapidly test their suitability for the platform. Support product discovery with technical prototypes, benchmarks, demos, and proof-of-concepts. Help define technical requirements, documentation, examples, and best practices for ML services. Qualifications: Strong engineering experience, ideally in ML infrastructure, developer tooling, cloud platforms, or AI products. Strong understanding of: ML workloads: training, fine-tuning, inference, model serving Infrastructure: GPUs, Kubernetes, containers, storage, networking Developer tooling: APIs, SDKs, CLIs, notebooks Distributed systems: scheduling, queues, retries, failures, scaling Familiarity with technologies such as Ray, Slurm, MLflow, Hugging Face, vLLM, Triton, LangChain, or similar. Comfortable moving quickly from an ambiguous customer problem to a working prototype or production implementation. Strong communication skills and comfortable working directly with product teams and customers. What you bring: Builder mindset: You prefer proving ideas with working software. Product sense: You care about usability and customer outcomes, not just technical correctness. ML fluency: You understand how real ML workloads behave in production. Technical breadth: You can work across application code, ML frameworks, APIs, and infrastructure. Speed and ownership: You can take an idea from prototype through to something customers can actually use. Our values: Set the standard: Every single day, you spot opportunities to constructively shake

Skills