Head of Artificial Intelligence

Space Dinosaurs

1h•Remote

About The Position

Momentum is seeking a deeply technical, product-minded Head of AI to own its core intelligence capabilities: Analyze, Recommend, and Learn. This hands-on builder-leader will guide product strategy and engineering execution, shaping how Momentum understands retailers, generates valuable recommendations, and learns from experiments and deployments. The role combines personally designing, prototyping, coding, evaluating, and shipping AI capabilities with recruiting and leading a lean team. The Head of AI will work directly with founders and collaborate with Build and Test engineering, balancing AI innovation, architecture, product priorities, customer outcomes, and technical execution. The position offers significant opportunity to expand as Momentum scales. The role owns the product vision, technical strategy, roadmap, and delivery of Momentum's intelligence capabilities. It involves integrating retail, customer, behavioral, and experimentation data; building AI systems and learning loops; translating complex data and ambiguous business problems into useful product experiences; and measuring system quality and business impact. The Head of AI will also work with customers, make practical technical decisions, and establish engineering and production practices while remaining hands-on. Momentum describes its environment as lean, fast-moving, distributed, and async-first, with high ownership and autonomy. The role is remote and offers the opportunity to influence the product's direction, architecture, and long-term differentiation while building its technical foundation and team.

Requirements

Proven success building and shipping production AI products or systems, ideally in a frontier AI lab, applied AI startup, or similarly demanding environment.
Deep practical expertise in foundation models, prompting, structured outputs, agents, orchestration, retrieval, evaluation, model adaptation, and current AI limitations.
Strong software engineering skills, including the ability to independently design, develop, review, and operate production code and data-intensive systems.
Strong product judgment, including MVP definition, prioritization, user discovery, technical tradeoffs, and measurable business outcomes.
Experience leading technical teams or initiatives, with the ability to recruit and mentor engineers while remaining an individual contributor.
Ability to work across AI experimentation, architecture, UX, business metrics, and hands-on debugging.
High agency, curiosity, pragmatism, adaptability, and comfort navigating early-stage ambiguity.
Excellent communication, collaboration, and low-ego technical leadership.

Nice To Haves

Experience with frontier model labs or early-stage AI-native startups.
Experience with recommendation or ranking systems, experimentation and causal inference, or sparse or heterogeneous feedback.
Experience in ecommerce, retail analytics, or conversion optimization.
Relevant experience with Python, TypeScript, cloud and data infrastructure, model APIs, open-weight models, agent frameworks, vector and relational databases, data pipelines, experimentation platforms, observability, or AI evaluation tools.
Commerce platform and analytics experience.

Responsibilities

Own the product vision, technical strategy, roadmap, and execution of Momentum's Analyze, Recommend, and Learn capabilities.
Integrate site, customer, behavioral, and experimentation data to identify opportunities and produce explainable, prioritized recommendations that improve through observed outcomes.
Design and implement LLM workflows, tool-using agents, retrieval and memory systems, structured data pipelines, feedback loops, evaluation frameworks, and supporting services.
Use historical and ongoing merchant A/B testing outcomes to develop learning capabilities while accounting for data quality, privacy, customer boundaries, and uncertainty.
Translate complex retail data and ambiguous business problems into actionable, trustworthy, measurable product experiences.
Establish offline and production evaluations for recommendation quality, ranking, grounding, cost, latency, reliability, and revenue impact; distinguish meaningful improvements from plausible outputs.
Evaluate build-versus-buy options, frontier APIs, open-weight models, fine-tuning, and traditional machine learning to select practical, scalable solutions.
Partner with Build and Test teams to connect recommendations with implementation, experimentation, attribution, and continuous learning.
Work directly with design partners and early customers to validate hypotheses, understand workflows, and iterate toward commercial value.
Recruit, mentor, and lead engineers while remaining hands-on; establish engineering standards, rapid feedback cycles, and disciplined production practices.
Communicate technical tradeoffs, priorities, and progress to founders, colleagues, customers, and investors when appropriate.