Director of Engineering - AI

CommerceIQ•Mountain View, CA

7h•$250,000 - $300,000•Onsite

About The Position

CommerceIQ’s AI-powered digital commerce platform is revolutionizing the way brands sell online. Our unified ecommerce management solutions empower brands to make smarter, faster decisions through insights that optimize the digital shelf, increase retail media ROI and fuel incremental sales across the world’s largest marketplaces. With a global network of more than 900 retailers, our end-to-end platform helps 2,200+ of the world’s leading brands streamline marketing, supply chain, and sales operations to profitably grow market share in more than 50 countries. 10 out of the top 12 CPG brands work with us, including Coca-Cola, Nestle, Colgate-Palmolive, and Mondelez. We’ve raised over $200M from some of the top investors including SoftBank, Insight Partners, and Madrona. Learn more at commerceiq.ai. We're looking for an Engineering Leader with a Data Science / Machine Learning background to head the incubation of CommerceIQ’s next generation of AI-driven SaaS solutions. The Head of AI Engineering is both hands-on and leadership-focused: you will act as a player-coach, directly shaping the technical architecture of our AI platform while building and mentoring a team of engineers. You will report directly to our cofounders to design and deliver advanced AI and agentic applications that push the boundaries of what is possible in applied AI for commerce. You will be accountable for scaling AI systems from research through production, ensuring that they create measurable business impact for the world’s largest brands. This role is ideal for a technical leader who has both trained models and shipped them into production, built agentic applications, and has a deep understanding of machine learning systems at scale. This is an onsite role based out our Mountain View, CA headquarters. You will be expected to work from the office Monday-Friday if hired.

Requirements

8-10 years of experience in backend software engineering, with 2+ years leading AI/ML teams.Hands-on experience training AI/ML models, including foundation models, fine-tuned models, or specialized domain models.
Proven experience building and deploying agentic applications, with deep knowledge of agent design patterns and orchestration.
Strong track record of launching AI models and agents into production environments at scale.
Advanced expertise in machine learning, deep learning, and reinforcement learning.
Experience designing scalable data systems and pipelines for training and inference.
Fluency in backend and ML-oriented programming languages such as Python, Java, Scala, or C++.
Knowledge of modern ML toolkits and frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, Ray, etc.).
Strong ability to balance hands-on technical depth with leadership, mentoring, and organizational building.
Excellent communication and influencing skills, with experience working directly with senior executives or founders.
Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field.

Responsibilities

Partner directly with the cofounders to set and execute the AI engineering strategy.
Hire, lead, and mentor a founding AI engineering team within our incubation group, growing both technical depth and organizational capability.
Act as a player-coach: contribute hands-on to architecture, model training, and backend engineering while scaling a team around you.
Design and build scalable ML infrastructure to support model training, fine-tuning, deployment, and monitoring at production scale.
Lead the development of agentic applications, including designing, training, and launching agents that operate in real-world customer environments.
Establish best practices for data quality, labeling, and ML pipelines, enabling rapid experimentation and continuous improvement.
Drive responsible AI development by ensuring reliability, ethics, safety, and performance in all models and agents shipped to production.
Collaborate closely with product, data science, and business stakeholders to translate research breakthroughs into customer-facing features.
Set engineering standards and build a culture of technical excellence, innovation, and accountability.