Senior Product Manager / ML Models

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Product Manager / ML Models based in the United States.

We are looking for a strategic and technically proficient product leader to shape the machine learning models powering advanced maritime awareness solutions. In this remote role, you will translate complex operational challenges into clear product strategies, model requirements, and measurable performance goals. You will work closely with machine learning engineers, customers, analysts, and company leadership to ensure models deliver meaningful results in real-world deployments. From experimentation and evaluation to launch and ongoing optimization, you will own the full model product lifecycle. This is a high-impact opportunity to connect AI innovation, robotics, data, and hardware within a mission-driven environment. Your work will help improve maritime safety, operational awareness, and the responsible management of ocean resources. \n

Accountabilities

Understand customer needs: Engage with users, analysts, and partners across different markets to understand how they detect, track, and respond to maritime activity. Translate findings into prioritized problem statements, user journeys, and measurable success criteria.

Define the ML product strategy: Establish the model portfolio and roadmap, determining which capabilities to develop, their priority, and the performance standards they must achieve.

Translate needs into technical requirements: Define target metrics, acceptable error rates, latency and edge-computing constraints, data requirements, and evaluation datasets based on customer and operational needs.

Partner with engineering teams: Collaborate closely with Applied Machine Learning and Radio Frequency (RF) Machine Learning engineers on experiment design, evaluation methodologies, error analysis, and release criteria.

Drive model quality and readiness: Assess model performance, determine whether solutions meet deployment requirements, and guide iterative improvements until they deliver meaningful customer outcomes.

Communicate strategy and progress: Present model roadmaps, technical trade-offs, risks, and performance results to executive leadership, board members when needed, customers, and strategic partners.

Own end-to-end product execution: Lead model initiatives from initial concept through development, launch, deployment, and ongoing performance monitoring.

Build continuous improvement loops: Connect field performance and user feedback to data collection, labeling, model retraining, and subsequent product iterations.

Establish measurement standards: Develop metrics, experimentation practices, and evaluation frameworks that help engineering teams deliver models more efficiently and with greater confidence.

Coordinate cross-functional execution: Align machine learning, software, hardware, data, and operational teams around a cohesive technical roadmap and shared product objectives.

Requirements:

Education: A bachelor's degree in engineering, computer science, or a related technical discipline, or equivalent practical experience. A master's degree in an engineering or technical field is an additional advantage.

Product leadership experience: At least 5 years of experience in product management, program management, or technical leadership, including 2 or more years taking technical products from conception to launch.

ML and AI product expertise: Experience defining product requirements, success metrics, and evaluation approaches for machine learning or AI-powered products in close collaboration with ML engineers and data scientists.

Experimentation and analytics: Familiarity with A/B testing, offline model evaluation, and other data-driven experimentation methods used to guide product decisions.

Technical understanding: Working proficiency in Python and SQL, enabling you to analyze data, evaluate results, and engage in technically detailed discussions with engineers.

Applied machine learning knowledge: Hands-on experience or strong familiarity with predictive modeling, recommendation or ranking systems, or large language model (LLM) and agentic pipelines. Knowledge of frameworks such as scikit-learn or TensorFlow is beneficial.

Cross-functional leadership: Demonstrated ability to lead engineers, researchers, designers, and operational teams without relying on direct reporting authority.

Executive and customer communication: Proven ability to explain complex technical concepts clearly, brief senior stakeholders, and influence important product and business decisions.

Operational domain familiarity: Experience or familiarity with maritime, defense, aerospace, or national security environments, with the ability to translate real-world operational needs into technical specifications.

Strategic thinking and ownership: Strong prioritization, analytical reasoning, decision-making, and problem-solving skills, with a focus on measurable outcomes and product quality.

Adaptability: Comfort working in a fast-moving environment with evolving technical challenges, remote collaboration, flexible working hours, and occasional periods requiring increased availability.

Benefits:

Competitive salary: Compensation is offered, although the specific salary range is not provided in the job description.

Remote work: Work remotely from the United States, collaborating with colleagues through online tools.

Flexible working hours: Flexibility in structuring your workday, with occasional high-priority deadlines requiring additional availability.

Professional development: Opportunities to expand technical and product management expertise.

Continued education: Support for ongoing learning and professional growth.

Mission-driven impact: The opportunity to contribute to innovative AI and robotics technologies designed to improve maritime awareness, safety, and ocean resource management.

Cross-disciplinary collaboration: Work at the intersection of machine learning, software, hardware, data, and real-world operational applications.

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