Sr. Staff Technical Product Manager

Groq•San Francisco, NY

1d•Hybrid

About The Position

Groq is building a global cloud platform for AI inference, powered by its LPU processor, which was designed specifically for inference. The Sr. Staff Technical Product Manager will shape the future of AI inference by owning product strategy and execution for critical areas of Groq’s AI Inference Platform. The role turns complex infrastructure challenges into scalable, reliable, and intuitive product experiences, partnering closely with Engineering and cross-functional teams to define products and drive them from concept through delivery. The position influences product direction across the platform and includes hands-on ownership of products that make high-performance AI inference more accessible to customers. The role is based in the Dallas, San Francisco, or New York City area. The employee may work remotely while Groq establishes a local office, after which the role is expected to transition to onsite. The total cash salary range for U.S.-based candidates is $310,400–$365,200, inclusive of potential bonus value. Individual placement depends on location, experience, skills, and internal compensation standards. International compensation varies by local market. Groq also offers a Long-Term Incentive Program and employee benefits. The position may require access to export-controlled technology or information. Candidates must meet applicable U.S. or other relevant export-control eligibility criteria. Groq is an equal opportunity employer and provides reasonable accommodations to qualified individuals with disabilities.

Requirements

10+ years of Product Management experience, including at least 5 years leading highly technical platform, infrastructure, or cloud products.
Deep domain expertise and demonstrated ownership of technically complex products in AI inference, cloud infrastructure, or related environments such as inference engines, LLM serving platforms, neoclouds, or AI cloud services.
Strong understanding of distributed systems, cloud infrastructure, APIs, Kubernetes, observability, platform reliability, capacity planning, and multi-region architectures.
Proven ability to independently define and deliver complex technical products, including developing specifications, prioritizing features, and driving execution with engineering teams.
Strong technical judgment and experience partnering with senior engineering leaders to translate customer needs into product strategy, influence architectural decisions, and deliver scalable infrastructure capabilities.
Highly self-directed, comfortable navigating ambiguity and making decisions, and able to drive progress with minimal oversight.
Strong analytical and problem-solving skills and a data-driven approach to prioritization and decision-making.
Excellent written and verbal communication skills, including the ability to influence engineering teams, cross-functional stakeholders, and executive leadership.
Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Must be based in the Dallas, San Francisco, or New York City area.
Must meet applicable export-control eligibility criteria for access to controlled technology or information.

Nice To Haves

Familiarity with infrastructure utilization, fleet scheduling, capacity forecasting, cloud economics, and platform operations.
Experience with consumption-based cloud services for enterprise customers, AI accelerators, or hardware and system bring-up.

Responsibilities

Drive product definition and delivery by translating customer needs, technical constraints, and business priorities into specifications, feature requirements, success metrics, and execution plans from concept through launch and continuous improvement.
Apply AI infrastructure and cloud platform expertise to complex initiatives, contributing to technical specifications, architecture discussions, and roadmap priorities across product workstreams.
Partner with Platform Engineering, Distributed Systems, Cloud Infrastructure, Reliability Engineering, Hardware Engineering, and customer-facing teams to deliver scalable, reliable, customer-centric platform capabilities.
Work with engineering leadership to navigate technical tradeoffs and balance customer impact, technical investment, platform scalability, and long-term architectural evolution.
Shape customer-facing cloud capabilities, including inference APIs, developer workflows, and platform experiences, so services are easy to adopt, deploy, and scale.
Define and prioritize internal platform investments in cloud infrastructure, resource management, observability, and platform optimization to improve scalability, reliability, operational efficiency, and infrastructure utilization.
Lead customer discovery with enterprise AI customers and developers to understand inference workloads, deployment workflows, performance expectations, operational challenges, and emerging infrastructure needs.
Use customer feedback, telemetry, and operational metrics to measure product adoption, customer experience, and platform performance, and continuously improve customer outcomes.
Collaborate with Sales, Solutions Engineering, Customer Success, and Marketing on product launches, customer adoption, and go-to-market initiatives.
Monitor the AI inference and cloud infrastructure landscape to identify emerging technologies, customer needs, and opportunities for product differentiation.
Communicate product strategy, roadmap, priorities, risks, and progress to executive leadership and cross-functional stakeholders.