Forward Deployed AI Strategy Lead

Responsibilities

• Lead strategic customer deployments from technical discovery through proof of concept, production deployment, expansion, and case study.
• Identify high-value AI workflows involving agents, complex automation, model improvement, inference-cost reduction, domain-specific evaluations, and frontier-scale post-training.
• Translate ambiguous customer problems, artifacts, goals, and constraints into executable scopes covering use cases, success metrics, evaluation design, environments, integrations, milestones, commercial structure, risks, dependencies, and expansion paths.
• Partner with Applied Research to prioritize evaluations, environments, agents, and post-training recipes that advance research and deliver customer outcomes.
• Build repeatable customer-deployment processes, including discovery templates, qualification frameworks, proof-of-concept structures, proposal language, pricing inputs, reference architectures, case studies, technical narratives, and deployment playbooks.
• Own customer relationships and revenue progression across qualification, legal, scoping, proposals, procurement, proofs of concept, deployment, and expansion.
• Create clear follow-ups, coordinate senior stakeholders, and prevent deals from stalling in ambiguity.

Requirements

• Strong intuition for AI products and workflows.
• Ability to understand technical systems without requiring every detail to be pre-digested.
• Excellent written and verbal communication.
• Comfort working with executives, researchers, engineers, and operators.
• High agency, low ego, and ability to operate without an established playbook.
• Ability to manage multiple complex customer workstreams.
• Strong judgment about valuable deployments and strong commercial instincts.
• Deep curiosity about post-training, agents, evaluations, reinforcement learning, and AI infrastructure.

Nice-to-haves

• Experience in forward-deployed engineering, technical go-to-market, AI product strategy, applied AI, solutions architecture, early-stage startup operations, AI/infrastructure/devtools product management, ML engineering, applied research, AI engineering, or technical venture/investing.
• Experience with reinforcement learning, supervised fine-tuning, evaluations, agents, Model Context Protocol, LangGraph, DSPy, Stagehand, Browserbase, or tool-use workflows.
• Experience working with enterprise AI teams or frontier AI companies.
• Ability to interpret traces, product documentation, API documentation, or technical specifications and turn them into deployment plans.
• Experience writing proposals, customer memos, technical scopes, or launch narratives.
• Founder or early-stage startup experience.
• Strong network across AI startups, research labs, or enterprise software buyers.

Compensation and Benefits

• Competitive cash compensation and meaningful equity.
• Flexible work in San Francisco or hybrid-remote.
• Visa sponsorship and relocation support.
• Professional development budget.
• Team off-sites and conference attendance.
• Direct exposure to frontier AI labs, leading AI startups, and enterprise AI teams.