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 of
AI 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

We’re looking for product leaders who can operate at depth across both
systems and product
, and who are excited about building the foundation for the next generation of AI applications.

You likely have:

- Experience building
AI/ML platforms, inference systems, or developer-facing infrastructure
- Strong understanding of
distributed 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.

You’ll 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 you’re excited about building the systems that power the next generation of AI applications, apply now!