Applied AI Engineer (Product)

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Company Overview

A technology company is developing advanced vision AI technology that combines a specialized sensor capable of capturing molecular chemistry, temperature, and 3D shape with a Large World Model designed to provide intelligence for the physical world. The technology aims to move beyond conventional visual recognition by providing deeper understanding of physical environments and objects.

The organization has developed technologies for NASA/JPL, advanced sensing systems for U.S. defense applications, and core infrastructure for leading AI research. Its current work focuses on developing next-generation space- and airborne-based sensing systems.

The organization is seeking an Applied AI Engineer (Product) to transform physics-informed models, general-purpose AI, and ultraspectral data into secure, evidence-backed intelligence products. This hands-on individual contributor will develop agentic workflows, retrieval and tool integrations, reporting capabilities, and human-in-the-loop experiences that help customers make operational decisions.

Key Responsibilities

• Build production workflows that combine physics-informed models, LLMs, VLMs, world models, scientific code, data services, and deterministic rules through stable interfaces.
• Design durable execution systems covering state management, planning, model routing, retrieval, memory, tool use, retries, timeouts, checkpoints, human approval, and recovery.
• Create evidence-backed outputs that clearly distinguish source data, model inference, system-generated synthesis, user input, and unresolved uncertainty.
• Partner with product and design teams to develop workflows for review, correction, comparison, approval, reporting, and recovery.
• Build context, retrieval, memory, and indexing patterns that account for permissions, data freshness, versioning, retention, and evidence lineage.
• Evaluate real-world system behavior across task success, groundedness, scientific validity, safety, latency, cost, and human intervention.
• Develop secure and reliable AI-powered product capabilities that support customer-facing operational workflows.
• Build reusable product-intelligence components that reduce one-off integration work while maintaining user control and scientific evidence.

Required Qualifications

• Experience building production AI applications, agent systems, workflow engines, backend services, or complex software products.
• Strong hands-on engineering skills across APIs, stateful services, databases, asynchronous execution, testing, debugging, and production operations.
• Practical experience with LLMs or multimodal systems, retrieval, memory, tool use, model routing, structured generation, and common AI system failure modes.
• Strong product judgment and the ability to connect technical design decisions to the user decision or workflow being improved.
• Experience building systems with security, permissions, data isolation, auditability, or human-approval requirements.
• Ability to design and operate reliable AI systems in production environments.
• Strong understanding of software engineering principles and production system development.

Preferred Qualifications

• Experience with scientific, geospatial, industrial, defense, autonomy, or other high-consequence intelligence systems.
• Experience building multimodal applications combining imagery, maps, time series, documents, structured data, or sensor streams.
• Experience with agent evaluation, constrained generation, program synthesis, durable workflow systems, or mixed cloud and edge architectures.
• Experience working closely with product and design teams to ship customer-facing capabilities.
• Familiarity with scientific or physics-informed AI systems and sensor-driven intelligence applications.

Success in This Role

• Customers receive timely, evidence-backed answers and workflows that support real operational decisions.
• Agentic capabilities behave predictably across failures, version changes, unavailable dependencies, and human-review boundaries.
• Reusable product-intelligence components reduce one-off integration work without obscuring user control or scientific evidence.

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