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FriendliAI is seeking a Director of Product Management to own the product strategy and evolution of their AI inference platform. This is a hands-on product leadership role for a technically strong leader who understands AI infrastructure and can translate complex systems into products customers want to use.
You will define how FriendliAI's inference platform serves models, scales across demanding workloads, and delivers measurable gains in performance, reliability, and cost. AI inference is an unusually complex product problem—models, GPUs, networking, scheduling, serving systems, and developer workflows must work together under real production constraints. Customers expect low latency and high throughput as workloads constantly change, and the platform must make this complexity feel simple to engineers building on top of it.
Key responsibilities include:
- Own the product vision, strategy, and roadmap for FriendliAI's AI inference platform, spanning model serving, deployment, orchestration, APIs, and developer-facing capabilities
- Define product direction across the inference stack, balancing performance, scalability, reliability, usability, and cost
- Identify and prioritize high-impact product opportunities across rapidly evolving AI workloads, including LLMs, multimodal models, and agentic applications
- Develop deep understanding of AI inference workloads and challenges faced by ML engineers, AI platform teams, and infrastructure organizations
- Partner closely with engineering and research to shape capabilities across routing, batching, scheduling, resource allocation, model optimization, and inference performance
- Drive product strategy around critical inference metrics including latency, time-to-first-token, throughput, GPU utilization, cost per inference, and availability
- Work directly with enterprise customers and technical users to understand production workloads and validate product direction
- Lead and mentor Product Managers and Product Designers, establishing clear ownership and high standards for product thinking and execution
- Establish product planning, prioritization, and measurement practices that help the organization move quickly while maintaining strong product judgment
- Define and track product KPIs and use customer feedback, product usage, and performance data to guide investment and prioritization
- Communicate product vision, priorities, and tradeoffs clearly to engineering, research, GTM, and executive leadership
FriendliAI is the fastest inference cloud for agents, built to run frontier open-weight models in production at scale. They deliver up to 7x faster output token speed, up to 90% lower inference costs, and 99.99% uptime across demanding agent workloads—long-context inference, real-time streaming, and accurate tool calling.
QUALIFICATIONS:
- 10+ years of product management experience, with significant experience owning technically complex platforms or infrastructure products
- Proven experience leading product strategy for AI/ML infrastructure, cloud platforms, distributed systems, developer platforms, or related technical products
- Deep understanding of AI inference and LLM serving architectures, including concepts such as prefill vs. decode, KV cache, token streaming, batching, routing, and GPU utilization
- Strong technical fluency and ability to work directly with senior engineering and research teams on complex distributed systems
- Proven track record taking technically complex products from strategy through execution and customer adoption
- Experience leading and developing Product Managers and/or coordinating multiple product workstreams
- Strong customer orientation, with experience working directly with enterprise customers and technical users
- Strong written and verbal communication, with ability to explain complex technical systems and make clear product tradeoffs
- Strong product judgment and ability to operate effectively in a fast-moving environment with significant ambiguity
PREFERRED EXPERIENCE:
- Experience with AI inference platforms, LLM serving, GPU infrastructure, or model deployment
- Familiarity with modern inference frameworks such as vLLM, TensorRT-LLM, SGLang, or similar technologies
- Experience building developer platforms, APIs, SDKs, or cloud-native infrastructure products
- Experience with multi-tenant SaaS or enterprise AI platforms
- Experience working with ML engineers, AI platform teams, or infrastructure organizations
- Background in Computer Science, Engineering, or closely related technical field
