Carmella Barrett

AI Product Manager | AI Infrastructure Germany | Hybrid from Heidelberg  You’ll own the product that decides where and when AI workloads run. This is a chance to take ownership of an AI Scheduling product at a growing AI infrastructure business. You’ll define the multi-year product vision, decide what gets built and work directly with engineers, infrastructure teams and enterprise customers to solve the challenges of running AI at scale.   The product sits around some of the hardest practical problems in AI infrastructure: how jobs are queued, how expensive GPU resources are allocated, and how latency, reliability and performance are managed as workloads grow.   You’ll have real ownership. Rather than managing a quarterly backlog, you’ll shape the longer-term direction of the product, turn customer problems into clear product decisions and work closely with leadership and engineering to get those decisions into production. You’ll also have direct customer exposure, giving you the opportunity to understand how organisations are actually using AI infrastructure and feed those insights back into the product roadmap.   This could suit you if you’re already a Technical Product Manager working on cloud infrastructure, platforms, developer tooling or cluster management and want to take ownership of a product at the centre of the AI infrastructure market.   You’ll need: 5+ years of Product Management experience, including ownership of infrastructure, platform or developer tooling products Experience with technologies such as Kubernetes, OpenStack, Slurm, VMware or similar A good understanding of cloud, compute infrastructure and how AI/ML workloads are run Experience creating longer-term product roadmaps and working closely with engineers and technical customers A technical background through education or hands-on experience   Experience in AI/ML infrastructure or MLOps would be particularly relevant. You’ll be based in Germany and able to work from the Heidelberg office on a hybrid basis, with up to 30% travel between Germany and the US.   If you’re interested in owning a product that sits at the intersection of AI, infrastructure and resource management, I’d be happy to tell you more.

London | Remote You’ll take ownership of the product that helps businesses build, train and deploy machine learning models at scale. This is an opportunity to lead product direction within a growing AI infrastructure business. You’ll define the longer-term vision, decide what gets built and work closely with engineers, ML specialists and customers to improve how AI models are developed and operated. The product covers some of the biggest challenges in machine learning infrastructure: how models are trained and fine-tuned, how GPU resources are used, and how models are deployed and served efficiently in production. You’ll have genuine product ownership. Rather than simply managing a delivery backlog, you’ll shape the roadmap, identify opportunities to improve the platform and turn complex technical and customer requirements into clear product decisions. You’ll also work closely with engineering and customers, gaining direct insight into how organisations are building and deploying AI and using that feedback to influence the future direction of the platform. This could suit you if you’re already a Technical Product Manager working on ML platforms, cloud infrastructure, MLOps or developer tooling and want to take ownership of a product in a fast-moving AI market. You’ll need: 5+ years of Product Management experience, ideally owning technical platform or infrastructure products Experience with ML platforms, MLOps, AI infrastructure or cloud-based developer products A good understanding of machine learning workflows, including model training, deployment and inference Knowledge of technologies such as Kubernetes, PyTorch, Ray, Slurm or similar Experience defining product strategy, building longer-term roadmaps and working closely with engineering teams and technical customers A strong technical understanding through education or hands-on experience Experience with GPU infrastructure, distributed computing or model serving would be particularly relevant. London | Remote If you’re interested in owning a product at the intersection of AI, machine learning and infrastructure, I’d be happy to tell you more.