Director of AI/ML

WasteCube is seeking a Director of AI & Machine Learning to define and lead the intelligence architecture behind our next-generation waste and resource-recovery systems.
This role will lead the design and development of the AI, machine learning, perception, data, and decision-making systems that enable WasteCube technologies to understand physical environments, interpret multimodal sensor data, make intelligent decisions, and continuously improve through real-world operation. You will work across AI/ML, robotics, software, sensing, simulation, data infrastructure, and edge computing to translate the WasteCube vision into scalable intelligent systems.
This is a zero-to-one technical leadership role. Some of the technologies required already exist and can be adapted. Others will need to be integrated in new ways, extended beyond their current applications, or developed specifically for WasteCube. We are looking for an AI leader who can determine the difference and build accordingly.
What You Will Be Doing
Define the overall AI and machine learning architecture for WasteCube systems.
Lead development strategy for AIRNN, WasteCube’s foundational AI system, including shared intelligence, learning, model orchestration, and long-term platform capabilities.
Define the architecture for WasteBuddy AI, the specialized intelligence layer supporting waste identification, user interaction, device intelligence, and operational decision-making.
Develop AI strategies for multimodal perception, including computer vision, depth sensing, weight, force, environmental sensors, material signatures, telemetry, and other sensor inputs.
Lead development of machine-learning systems for object and material recognition, contamination detection, anomaly detection, classification, prediction, optimization, and confidence scoring.
Define how AI systems interact with robotics, embedded systems, controls, mechanical systems, and physical processing equipment.
Partner closely with robotics leadership to translate perception and machine decision-making into reliable physical action.
Work with FORGE software and simulation teams to develop digital twins, synthetic-data environments, virtual testing, model evaluation, and simulation-driven development.
Establish AI training, validation, deployment, monitoring, and model-lifecycle frameworks.
Define edge, local, and centralized inference strategies across WasteBuddy, WasteCube, WasteDriver, WasteHub, and related infrastructure.
Develop strategies for continual learning, fleet learning, model improvement, and feedback from deployed systems.
Help define WasteCube’s proprietary data strategy and identify opportunities to create defensible AI and data assets.
Evaluate emerging AI technologies, models, frameworks, compute platforms, and infrastructure for integration into WasteCube systems.
Determine where WasteCube should leverage proven AI technologies versus develop proprietary models, systems, or methods.
Help build and lead the AI and machine learning organization as WasteCube moves from pre-seed development into full engineering execution.
What We Need To See
Significant experience in artificial intelligence, machine learning, applied AI, computer vision, perception, intelligent systems, or related technical fields
Strong systems-architecture experience across AI/ML, software, data, cloud, edge computing, and intelligent system domains
Experience taking AI or machine-learning systems from research or experimentation through validation, deployment, or production
Strong understanding of model development, training, evaluation, inference, monitoring, and lifecycle management
Experience working with multimodal data, sensor data, computer vision, perception, or other complex real-world datasets
Strong understanding of data pipelines, model infrastructure, distributed systems, and AI deployment environments
Ability to reason from first principles and operate effectively in ambiguous, early-stage technical environments
Strong technical leadership and communication skills across multidisciplinary teams
Experience mentoring engineers, researchers, or contributing to the development of high-performing AI/ML teams
Ways To Stand Out
Experience with robotics, embodied AI, autonomous systems, or AI systems that directly influence physical machine behavior
Experience with computer vision, sensor fusion, multimodal learning, or perception systems
Experience with reinforcement learning, agentic AI, planning systems, adaptive decision-making, or autonomous intelligence
Experience building reusable AI platforms or shared machine-learning infrastructure rather than isolated models
Experience with simulation, synthetic data, digital twins, or virtual testing environments
Experience deploying AI across edge, cloud, and distributed systems
Experience with fleet learning, continual learning, or systems that improve through distributed real-world data
Experience with industrial automation, autonomous vehicles, manufacturing, aerospace, defense, logistics, robotics, or other complex physical systems
Previous startup, R&D, or zero-to-one product development experience
A track record of building systems that required creating new technical approaches rather than simply implementing established ones
Work Structure
This is a full-time role that will begin remote during WasteCube’s initial architecture and engineering phase. For approximately the first 4–6 months, depending on development progress and the requirements of the position, the role is expected to focus on AI architecture, system design, simulation, model strategy, data architecture, virtual testing, early development, and integration planning.
As WasteCube moves into physical prototyping, Alpha testing, integration, and real-world system development, this position will require periodic collaboration with engineering teams and physical systems in Florida. The role is expected to remain more remote-flexible than many hardware-focused positions, but direct involvement in integration, testing, and major development milestones will become increasingly important. As the development program expands, relocation assistance and/or relocation bonuses may be available for team members who choose to relocate.
About WasteCube
WasteCube is developing a new generation of intelligent waste infrastructure designed to transform how waste is identified, sorted, processed, and recovered. By combining robotics, artificial intelligence, advanced sensing, automated material handling, and energy-recovery technologies, WasteCube is building modular systems capable of turning waste streams into recoverable materials, usable energy, and actionable data.
Our vision extends from residential and commercial environments to institutions, municipalities, industrial sites, and government applications. Rather than treating waste as an end-stage problem, WasteCube is building toward a distributed, intelligent infrastructure platform that can reduce landfill dependence, improve material recovery, lower operating costs, and create a smarter circular economy.
Currently in pre-seed development, WasteCube is focused on technical validation, system architecture, and prototype development as we assemble the engineering leadership required to bring the platform from concept to deployment.

Work arrangement
Yes