Senior Product Manager / ML Models

At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.

Job Description:

We are looking for a Senior Product Manager, ML Models to own the product strategy for the machine learning models that power our maritime domain awareness products. You will sit between our customers, our Machine Learning Engineers, and our company leadership. You will turn ambiguous operational problems into a clear model roadmap, help the team iterate on those models until they work in deployments, and make sure what we build fits a cohesive technical roadmap across hardware, software, data, and ML. This is a high-ownership role for someone who is equally comfortable in a model evaluation review and in a briefing with a senior executive or customer. Key Responsibilities:

Spend time with users, analysts, and partners across a wide variety of customer markets to understand how they detect, track, and decide about maritime activity today. Turn what you learn into prioritized problem statements, user journeys, and measurable success criteria.

Define the model portfolio, covering what to build, in what order, and to what performance bar. Turn customer outcomes into model requirements: target metrics, acceptable error modes, latency and compute budgets at the edge, data needs, and evaluation datasets.

Work day to day with Applied ML and RF ML Engineers on experiment design, evaluation methodology, error analysis, and release criteria. Decide when a model is good enough to ship and when it needs another iteration. Hold a high bar for quality and a clear picture of what "better" means to customers.

Communicate model strategy, progress, tradeoffs, and risks to the C-suite, the board as needed, and customers and partners. Make complex technical topics understandable and decision-ready.

Own execution from ideation through launch and ongoing performance monitoring. Build feedback loops from fielded systems back into data collection, labeling, and retraining.

Establish metrics, experimentation practices, and evaluation standards that let the team ship models faster and with more confidence.

Qualifications (Preferred):

Bachelor's degree in engineering, computer science, or a related technical field, or equivalent practical experience.

5+ years of experience in product management, program management, or technical leadership roles, including at least 2 years taking technical products from conception to launch.

Experience defining requirements, success metrics, and evaluation approaches for ML or AI-powered products, working directly with ML engineers and data scientists.

Experience running experiments and using data to drive product decisions (A/B testing, offline evaluation, or similar).

Demonstrated ability to lead cross-functional teams of engineers, researchers, designers, and operators without direct authority.

Track record of 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 depth with engineers.

MS in an engineering or technical discipline, or both.

Hands-on experience with applied ML, such as predictive modeling, recommendation or ranking systems, or LLM/agentic pipelines, and familiarity with frameworks such as scikit-learn or TensorFlow.

Background or familiarity with maritime, defense, aerospace, or national security environments, and comfort translating operational needs into technical requirements.

Work Environment: This is a remote position with collaboration via online tools.

Flexible working hours with occasional deadlines requiring high availability.

Opportunity to work on innovative projects with a global impact.

Benefits

: Competitive salary

Flexible work hours and the option for remote work.

Opportunities for professional development and continued education.