Senior Product Manager - LLM Inference
New
R
RunwareLLM inference
United KingdomFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- Product Analytics
Requirements
- Proven product ownership in a technical, developer-facing environment.
- Experience leading API products, developer platforms, AI infrastructure, or similarly technically complex offerings from strategy and development through launch, adoption, and commercial growth.
- Strong understanding of production LLM inference, including model capabilities and serving trade-offs across quality, context length, token consumption, latency, throughput, reliability, and cost.
- Deep understanding of developer experience and API products, including integration, production scaling, API design, compatibility, migration, SDKs, and documentation.
- Strong commercial and product judgment, including balancing customer requirements, technical constraints, market opportunities, pricing, and unit economics.
- Experience working directly with technical customers and leading customer discovery.
- Hands-on technical aptitude testing APIs, exploring product behavior, analyzing usage data, building prototypes, and discussing implementation trade-offs with engineers.
- Strong execution and cross-functional leadership, including navigating ambiguity, aligning stakeholders, and taking accountability from discovery through delivery and measurable results.
- Direct experience building or scaling an LLM inference API, model-serving platform, or AI infrastructure product is a differentiator.
- Knowledge of GPU inference and model-serving technologies such as batching, quantization, KV caching, capacity planning, vLLM, SGLang, or TensorRT-LLM is a differentiator.
- Experience with LLM evaluation, benchmarking, production applications, usage-based pricing, token metering, billing systems, inference unit economics, or dedicated capacity commitments is a differentiator.
- Familiarity with enterprise requirements for data security, residency, observability, and service reliability; prior hands-on experience in software engineering, machine learning engineering, infrastructure, or developer tooling is a differentiator.
Responsibilities
- Own the LLM inference product strategy and roadmap across model availability, API capabilities, production readiness, and customer adoption.
- Lead customer discovery with developers, AI teams, and technical decision-makers; evaluate production workloads and adoption barriers.
- Guide model discovery, evaluation, launches, and deprecations across open-weight, proprietary, custom, and fine-tuned models.
- Shape API capabilities and developer experience, including compatibility, SDKs, documentation, streaming, tool calling, structured outputs, caching, and batch inference.
- Define production inference requirements with Engineering, including performance, reliability, serving efficiency, dedicated capacity, rate limits, routing, fallbacks, and observability.
- Partner with go-to-market teams on positioning, pricing, packaging, technical evaluations, and customer adoption.
- Work with Engineering and Security to define enterprise capabilities such as data residency, retention controls, and service commitments.
- Write requirements, prototype solutions, test APIs, reproduce customer issues, and work with engineers to resolve technical constraints.
- Track activation, production usage, retention, expansion, revenue, gross margin, and service quality; refine priorities using customer feedback and product data.
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