Forward Deployed AI Strategy Lead

Company Overview

The organization develops an open, full-stack artificial intelligence infrastructure platform designed to help AI research teams, enterprises, and technology companies build, train, evaluate, and deploy advanced AI systems. Its platform supports compute infrastructure, evaluation environments, secure sandboxes, high-performance training, and deployment workflows, including supervised fine-tuning, reinforcement learning, tool use, and agent development.

Responsibilities

Strategic Customer Deployments

• Lead high-priority customer engagements from initial technical discovery through proof of concept, deployment, expansion, and case study development.
• Work with customers building AI agents, automating complex workflows, improving model performance, reducing inference costs, developing domain-specific evaluations, and implementing large-scale post-training.
• Help customers determine which tasks to train or evaluate, how to define success, and which workflows should become structured AI environments.
• Identify required data, execution traces, automation opportunities, supervision requirements, and measurement approaches.
• Recommend suitable model adaptation strategies and define pathways from prototype development to production deployment.

Technical Discovery and Project Scoping

• Translate ambiguous customer requirements, internal documentation, product objectives, and technical constraints into clearly defined implementation plans.
• Define use cases, success metrics, evaluation strategies, environment requirements, and integration needs.
• Establish project milestones, commercial structures, risks, dependencies, and potential expansion opportunities.
• Ensure that technical scopes are actionable and aligned with the capabilities of Applied Research and Engineering teams.

Applied Research Collaboration

• Partner closely with Applied Research to translate customer feedback and real-world requirements into research priorities and product roadmap opportunities.
• Identify evaluation methods, environments, agents, and post-training approaches that can advance AI capabilities while delivering measurable customer outcomes.
• Explore methods for converting real-world workflows into reliable reinforcement learning environments.
• Assess evaluation quality, verifier reliability, task trainability, and the effectiveness of managed reinforcement learning compared with prompting or manual workflow design.
• Develop approaches to demonstrate measurable model performance improvements to customers.

Repeatable Deployment and Commercial Strategy

• Develop repeatable processes that make strategic customer deployments more efficient and scalable.
• Create discovery templates, customer qualification frameworks, proof-of-concept structures, and deployment playbooks.
• Contribute to proposals, pricing and packaging strategies, reference architectures, case studies, and technical narratives.
• Document successful deployment approaches and turn lessons learned into reusable processes.
• Help establish a scalable operating model for delivering applied AI solutions and expanding customer relationships.

Qualifications

The provided job description does not specify formal degree requirements, minimum years of experience, or mandatory certifications. Candidates should be prepared to demonstrate relevant capabilities in the following areas:

• Ability to translate complex business workflows into clearly scoped AI implementation opportunities.
• Understanding of AI systems, model evaluation, post-training, and agent-based workflows.
• Ability to define measurable success criteria and develop structured approaches to evaluating AI performance.
• Experience coordinating technical deployments across customer-facing, research, and engineering teams.
• Strong problem-solving skills and the ability to navigate ambiguous requirements and technical constraints.
• Commercial awareness, including the ability to connect technical outcomes with customer value, revenue opportunities, and long-term expansion.
• Strong written and verbal communication skills for customer discovery, technical scoping, proposals, and stakeholder alignment.
• Ability to develop repeatable frameworks, documentation, and deployment processes that improve future engagements.

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