Technical Product Manager - AI Compute Platform

Technical Product Manager – AI Compute Platform (Germany) This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Technical Product Manager – AI Compute Platform based in Germany. In this role, you will help shape and scale a next-generation AI cloud platform powering some of the most demanding machine learning workloads in the world. You will own critical parts of a hyperscale infrastructure product, working at the intersection of engineering, customers, and platform strategy. This is a deeply technical product management role where you will collaborate as a peer with senior engineers on topics such as GPU orchestration, distributed training, cluster operations, and cloud APIs. You will translate complex customer needs into robust platform capabilities that enable large-scale AI training and inference. The environment is fast-paced, highly technical, and mission-driven, with direct impact on how frontier AI systems are built and deployed. This role is ideal for someone who thrives in ambiguity, enjoys solving infrastructure-scale problems, and wants to define the future of AI compute platforms. Accountabilities Own end-to-end product strategy, roadmap, and execution for a critical slice of an AI compute platform, ensuring alignment with customer and business outcomes. Define and evolve platform contracts such as APIs, system behaviors, lifecycle semantics, and developer-facing interfaces at hyperscaler quality. Lead cross-functional execution across engineering, SRE, networking, storage, observability, IAM, billing, capacity planning, and customer-facing teams. Drive structured product discovery through customer interviews, usage analytics, incident analysis, and support feedback loops. Translate complex technical and operational challenges into clear product requirements and measurable success metrics. Collaborate as a technical peer with engineering teams to evaluate architecture decisions, system trade-offs, and platform design quality. Own adoption and performance of shipped features, ensuring continuous improvement based on real-world usage and telemetry. Serve as the escalation point for customer-facing teams on product behavior, system reliability, and platform design decisions. Define success metrics tied to customer impact, platform efficiency, and operational excellence rather than output-based delivery. Requirements 6+ years of experience in Product Management, Platform/Product Infrastructure roles, or equivalent experience in SRE or Engineering leadership with strong product ownership. Strong technical foundation in cloud infrastructure, distributed systems, or AI/ML platforms, with the ability to reason about system design and architecture. Experience working with or operating large-scale infrastructure such as GPU clusters, HPC systems, or multi-tenant cloud environments. Proven track record of shipping complex technical products with measurable impact on customers or platform performance. Strong analytical skills with experience defining metrics, working with telemetry, and driving data-informed product decisions. Experience leading discovery processes, including customer interviews, usage analysis, and support-driven insights. Ability to engage confidently with engineering teams on topics such as API design, system reliability, control planes, and distributed systems behavior. Excellent communication and stakeholder management skills across engineering, product, operations, and executive teams. High ownership mindset with a strong bias toward execution, iteration, and operational excellence. Familiarity with GPU infrastructure, Kubernetes, Slurm, or HPC environments is highly desirable. Experience with distributed ML training or inference workloads (e.g., multi-node training, NCCL, checkpointing, fault-tolerant systems) is a strong plus. Exposure to cloud platforms at hyperscaler scale (AWS, GCP, Azure) and developer experience design (APIs, CLI tools, observability systems) is advantageous. Understanding of reliability engineering practices, SRE principles, and operational metrics such as MTTR, MTBF, or system-level goodput is a plus. Experience working with emerging AI workloads such as agentic systems, RL pipelines, or large-scale inference serving is considered a bonus. Benefits Competitive compensation package. Opportunity to shape foundational infrastructure for the global AI ecosystem. High-impact role with ownership over critical components of a hyperscale AI platform. Flexible, trust-based work environment with strong autonomy. Career growth in a fast-scaled, engineering-driven organization. Exposure to cutting-edge AI, GPU, and distributed systems technologies. Collaborative international environment with world-class engineering teams. Opportunity to work on problems at the frontier of cloud infrastructure and AI compute. #J-18808-Ljbffr Jobgether