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Fundamental is an AI research lab developing NEXUS, a Large Tabular Model (LTM) purpose-built for structured enterprise data. The company has raised $275M and is trusted by Fortune 100 companies.
You will own the complete product strategy and roadmap for NEXUS inference offerings, focusing on customer adoption, performance, reliability, and economics. This is a senior, hands-on individual contributor role that combines strategic thinking with direct customer engagement and cross-functional partnership.
Key responsibilities include:
- Developing and executing inference strategy: Own the full lifecycle from strategy through delivery of NEXUS inference offerings. Benchmark competitive landscape, translate customer needs into clear product offerings with transparent performance and cost tradeoffs, and partner with engineering on scope, dependencies, and delivery milestones.
- Managing inference economics: Drive adoption, usage optimization, and cost efficiency for inference products. Use customer demand signals and serving costs to guide roadmap decisions in partnership with engineering and go-to-market teams.
- Direct customer discovery: Spend significant time with enterprise customers running models in production workloads. Collaborate with their ML, platform, and IT teams to translate constraints around latency, throughput, compliance, reliability, and cost into actionable roadmap priorities.
- Engineering partnership: Work daily with inference and platform engineering teams on capacity planning, product prioritization, and delivery timelines.
- Model operations and deployment: Own the product capabilities customers need to deploy and operate NEXUS reliably, including deployment versioning, controlled upgrades, rollback procedures, and monitoring/alerting infrastructure.
- Deployment offerings and pricing: Define how customers consume NEXUS across managed services and customer-controlled environments. Partner with product marketing and GTM on packaging, pricing, deployment, integration, upgrade, and support requirements.
- Enterprise administration: Own the product roadmap for administrative and governance capabilities, including customer portal, user and API key management, access controls, auditability, usage visibility, and data-handling controls.
REQUIREMENTS:
Must have:
- 7+ years of product management experience in model inference, infrastructure, developer platforms, or API products, with a track record of owning technical products from early development through launch with measurable adoption or revenue growth
- Strong understanding of inference, model serving, and cloud infrastructure; ability to evaluate architecture and API choices and translate latency, throughput, capacity, reliability, and cost tradeoffs into product decisions
- Strong commercial judgment and understanding of infrastructure economics; experience using customer needs, usage patterns, and cost drivers to shape product offerings and priorities
- Experience working directly with enterprise ML, platform, or IT teams to understand production requirements, distinguish reusable capabilities from customer-specific requests, and make difficult prioritization decisions
- Ability to turn ambiguous product goals into executable delivery plans with clear owners, milestones, and dependencies; track progress and resolve blockers through cross-functional launch
- Experience defining product success metrics, establishing analytics and tracking for adoption, usage, performance, reliability, and cost; using insights to identify risks and prioritize improvements
Nice to have:
- Direct experience owning inference or model-serving products
- Experience shaping pricing and packaging for technical products
- Previous software, infrastructure, or ML engineering background before transitioning to product management
- Experience bringing products into customer-controlled environments (e.g., customer VPCs) and addressing integration, security, administration, and support requirements
- Experience building enterprise security, identity and access management capabilities or integrations with data and ML platforms
