Senior Product Manager / ML Models

Quartermaster•Washington, DC

5d•Remote

About The Position

Quartermaster AI is seeking a Senior Product Manager, ML Models, to own product strategy for the machine learning models powering its maritime domain awareness products. The role sits between customers, Machine Learning Engineers, and company leadership, translating ambiguous operational problems into a clear model roadmap and ensuring models are iterated until they work in deployments. The position also ensures product development fits a cohesive technical roadmap across hardware, software, data, and machine learning. This is a high-ownership role suited to someone comfortable with both model evaluation reviews and briefings with senior executives or customers. The position is remote and involves collaboration through online tools, flexible working hours, and occasional deadlines requiring high availability.

Requirements

Bachelor's degree in engineering, computer science, or a related technical field, or equivalent practical experience.
At least 5 years of experience in product management, program management, or technical leadership roles.
At least 2 years of experience taking technical products from conception to launch.
Experience defining requirements, success metrics, and evaluation approaches for ML- or AI-powered products in collaboration with ML engineers and data scientists.
Experience running experiments and using data to make product decisions, such as A/B testing or offline evaluation.
Demonstrated ability to lead cross-functional teams of engineers, researchers, designers, and operators without direct authority.
Experience briefing senior leaders, including executives or customers, on complex technical topics and shaping decisions.
Working fluency in Python and SQL sufficient to analyze data and engage in technical discussions with engineers.

Nice To Haves

A master's degree in an engineering or technical discipline, or both bachelor's and master's degrees.
Hands-on applied ML experience, such as predictive modeling, recommendation or ranking systems, or LLM/agentic pipelines.
Familiarity with frameworks such as scikit-learn or TensorFlow.
Background or familiarity with maritime, defense, aerospace, or national security environments.
Comfort translating operational needs into technical requirements.

Responsibilities

Engage users, analysts, and partners across customer markets to understand how they detect, track, and make decisions about maritime activity.
Translate customer research into prioritized problem statements, user journeys, and measurable success criteria.
Define the model portfolio, including what to build, sequencing, and required performance levels.
Translate customer outcomes into model requirements, including target metrics, acceptable error modes, edge latency and compute budgets, data needs, and evaluation datasets.
Work with Applied ML and RF ML Engineers on experiment design, evaluation methodology, error analysis, and release criteria.
Decide whether models are ready to ship or require further iteration, maintaining a high quality bar and a clear understanding of customer-defined improvement.
Communicate model strategy, progress, tradeoffs, and risks to the C-suite, the board as needed, customers, and partners.
Own execution from ideation through launch and ongoing performance monitoring.
Build feedback loops from fielded systems into data collection, labeling, and retraining.
Establish metrics, experimentation practices, and evaluation standards to enable faster, more confident model releases.