Distinguished AI Tech Lead, Frontier

AVEVA is creating software trusted by over 90% of leading industrial companies.

Position: Distinguished AI Tech Lead, Frontier Applications

Locations: US (San Leandro, CA, Boston, MA, New York, NY)

Employment Type: Full-time

Salary Range: $ - $

We are seeking a visionary and accomplished AI Tech Lead, Frontier Applications to join our team. This high‑visibility, high‑impact leadership role drives the technical truth (e.g., model choices, logic and intent) and readiness requirements for AI capabilities. The leader partners across functions on our AI strategy and roadmap, shaping how we infuse AI into existing capabilities while balancing 0‑to‑1 amid ambiguity to inform future AI offerings.

We are specifically keen on individuals with deep understanding of emerging AI and ML technologies and can bridge between advanced AI, ML engineering and AI business.

• Shape and evangelize the technical truth (e.g., model choices, logic and intent) for AI capabilities from early concepts through launch, aligning with overall technology strategy and AI business goals, market trends, and customer needs.
• Deeply understand AI and ML advances, navigate ambiguity and make thoughtful trade‑offs in both 0‑to‑1 and ongoing lifecycle management environments to identify opportunities where AI can help address value‑driven and real‑world use case for industrial customers.
• Source emerging AI technologies for industrial AI applications.
• Proactively identify and help mitigate technical risks, and ensure quality, safety, and reliability best practices.
• Partner cross‑functionally, drive the creation of technical AI requirements documents and end‑to‑end technical roadmap, prioritizing features, data constraints, model performance, and requirements based on customer use cases, scientific and technological advancements, and broader industrial sector analysis.
• Define, monitor and report on key value‑driven AI capability metrics and determine necessary data‑driven insights to evaluate performance and inform future decisions.
• Apply strong technical, analytical and problem‑solving skills with a data‑driven approach to decisions.
• Ensure AI capabilities are industrial sector relevant and adhere to safety, security, governance and regulatory standards.
• Provide leadership and mentorship to junior team members; foster a culture of innovation and continuous improvement.

• 12+ years of experience with at least 5 years in an AI technical leadership role for both emerging 0‑to‑1 and established AI capabilities.
• Recent experience with modern AI paradigms such as world models, foundation models, multi‑modal language models; agent‑based systems and orchestration; and context retrieval and augmentation techniques.
• Experience in responsible deployment practices, model drift mitigation, privacy‑preserving federating learning, and AI governance best practices.
• Exceptional technical background with deep intuition for AI in highly regulated industries and proven experience having recently shipped code or emerging AI capabilities to production.
• Excellent judgment in prioritizing between emerging AI technologies and existing ones for right‑sized and industrial sector relevant features, architectural improvements and revenue entitlement.
• Expertise in Python and deep experience with deep learning frameworks (e.g., Jax, Tensor Flow, PyTorch) and state‑of‑the‑art ML techniques (building, training, evaluating, deploying ML models).
• Exceptional leadership and communication skills, with the ability to influence cross‑functional and matrixed teams, and present complex technical information clearly to both technical and non‑technical audiences.
• Master’s degree in Computer Science, Physics, Engineering or a related technical field;
  Ph.D. degree preferred.

• Experience in at least two regulated industries (e.g., life sciences, manufacturing, energy, infrastructure, asset‑intensive industries, material science, etc.).
• Experience analyzing technical, scientific and market data for emerging AI technologies.
• Experience deploying ML models in cloud environments (AWS, GCP or Azure) in addition to work across…