Product Manager - Ai Infrastructure

Turba Labs•Hamburg, Baden-Württemberg

102d•Hybrid

About The Position

Turba Labs is seeking an experienced Product Manager to define product vision and expand its AI infrastructure design and management platform. The role combines strategic planning with hands-on product work, developing frameworks and processes that incorporate insights from engineers, executives, and customers. The Product Manager will own the multi-year vision and roadmap for the AI Scheduling product, spanning job queuing, resource allocation, latency optimization, and reliability at scale. The role collaborates with engineering, infrastructure, and platform teams; engages enterprise and developer customers; monitors relevant competitive and technical developments; and works with leadership on launches, roadmap reviews, and product narrative. It also supports go-to-market work through messaging, documentation, and technical content. The position is open to candidates based in Germany who can work in a hybrid setup from the Heidelberg office, or candidates based in the San Francisco Bay Area. Travel of up to 30% between German and US locations may be required. Turba Labs describes its environment as a fast-growing startup with flat hierarchies, flexible hours, hybrid and remote-friendly options, an international team, modern offices, and a choice of high-quality hardware and equipment.

Requirements

At least 5 years of product management experience, including significant ownership of infrastructure, platform, or developer tooling products.
BS or MS in Computer Science or Engineering, or equivalent hands-on technical experience.
A track record of defining and delivering multi-year product roadmaps.
Product management experience with cloud or cluster management technologies such as Kubernetes, OpenStack, Slurm, VMware, or similar platforms.
Working understanding of machine learning infrastructure and software, including compute hardware, communication layers, models, inference engines, orchestration tools, and common user and developer workflows.
Ability to operate in ambiguity and make clear decisions with incomplete information.
Strong written and verbal communication skills and ability to work effectively with engineers, executives, and enterprise customers.
Comfort working as an individual contributor and creating impact through insight, ownership, and cross-functional influence.
Excellent organizational skills and ability to manage multiple workstreams while maintaining quality and momentum.
Willingness and ability to travel up to 30% between German and US locations as business needs require.
For Germany-based candidates, ability to work in a hybrid setup from the Heidelberg office; alternatively, candidates may be based in the San Francisco Bay Area.

Nice To Haves

Experience in an AI/ML infrastructure or MLOps environment, including understanding how scheduling supports training, inference, and evaluation pipelines.
Portfolio of technical writing, product specifications, or public content demonstrating clear communication of complex topics.
Measurable outcomes demonstrating ownership, such as shipped products, adoption metrics, customer wins, or performance improvements.

Responsibilities

Develop the multi-year vision and roadmap for the AI Scheduling product, covering job queuing, resource allocation, latency optimization, and reliability at scale.
Collaborate with engineering, infrastructure, and platform teams to define and deliver capabilities for enterprise and developer customers.
Engage customers to identify unmet needs, validate product direction, and turn complex or conflicting input into clear product decisions.
Monitor competitive and technical developments, including distributed systems, orchestration tools, and AI workload patterns, and translate relevant trends into roadmap implications.
Work with leadership on product launches, roadmap reviews, and the broader product narrative.
Create feedback loops between customers and engineering to improve performance, usability, and reliability.
Support go-to-market activities through product messaging, documentation, and technical content such as blogs, white papers, and presentations.