Senior Product Manager

Senior Product Manager: AI Platform & Managed Services ($600mill in funding) $250K + Equity San Francisco, Bay Area - Onsite 5 days We are building a next-generation AI platform that enables developers and enterprises to train, deploy, and operate large-scale machine learning systems across heterogeneous compute environments. Our focus is on making cutting-edge AI infrastructure usable, scalable, and efficient - abstracting away the complexity of distributed systems, multi-GPU orchestration, and model lifecycle management into a cohesive, developer-first platform. This is a high-impact role at the intersection ofAI infrastructure, distributed systems, and developer platforms , where you will define and build the product layer that sits between raw compute and real-world AI applications. You will lead product across key areas of our AI platform and managed services stack, including: Inference & Model Serving Platforms Design systems for high-throughput, low-latency inference across LLMs, diffusion models, and multimodal workloads Define abstractions for batching, scheduling, caching, and model optimization (quantization, compilation, etc.) Balance performance, cost, and reliability across diverse workloads AI Platform & Developer Experience Build APIs, SDKs, and workflows that enable developers to go from model ? production seamlessly Define primitives for fine-tuning, evaluation, deployment, and observability Simplify complex infrastructure into intuitive, composable building blocks Multi-Cluster / Multi-Vendor Compute Orchestration Work on scheduling and workload placement across heterogeneous environments (GPU/CPU, multi-region, multi-cloud) Partner with engineering on resource allocation, queuing systems, and capacity-aware scheduling Observability, Evaluation & Cost Governance Define telemetry systems for model performance, latency, token usage, and failure modes Build evaluation workflows for LLM quality, safety, and regression detection Introduce cost controls and optimization strategies for large-scale inference and training Managed AI Services Package infrastructure into opinionated, production-ready services for enterprise customers Define SLAs, reliability models, and deployment patterns for mission-critical workloads Work closely with customers to understand real-world constraints and translate them into product capabilities Were looking for product leaders who can operate at depth across bothsystems and product , and who are excited about building the foundation for the next generation of AI applications. You likely have: Experience buildingAI/ML platforms, inference systems, or developer-facing infrastructure Strong understanding ofdistributed systems, cloud infrastructure, and performance trade-offs Familiarity with modern AI stacks: LLMs, transformers, diffusion models Frameworks like PyTorch, TensorRT, ONNX, vLLM, Triton, etc. AI is undergoing a platform shift. The gap between raw infrastructure and usable systems is still enormous. This role is about closing that gap: turning fragmented, complex infrastructure into a coherent platform that developers can rely on to build real products. Youll be working on problems like: How to make LLM inference predictable and cost-efficient at scale How to expose the right abstractions for agentic workflows How to manage heterogeneous compute without leaking complexity to users How to make AI systems observable, debuggable, and reliable If youre excited about building the systems that power the next generation of AI applications, apply now!