Product Owner – Machine Learning

Machine Learning Engineer
Remote
June 5, 2026
Application ends: September 3, 2026

Job Description

REQUIREMENTS

  • Experience
  • 3-5 years of experience in product ownership, product management, or equivalent delivery focused roles.
  • Demonstrated experience supporting ML based products in production.
  • Direct experience working with data science and ML engineering teams.
  • Domain and Technical Fluency
  • Strong working knowledge of computer vision and ML fundamentals.
  • Experience with biometric technologies such as face matching, liveness detection, and spoof prevention.
  • Experience with document verification, document classification, or document fraud detection.
  • Hands-on experience building ML based products in the biometric and document identity space is highly valuable.
  • Ability to evaluate tradeoffs between different modeling and data approaches without being a data scientist.
  • Execution and Judgment
  • Comfortable pushing back on ML teams when solutions are over engineered, misaligned, or not production ready.
  • Able to propose alternate approaches grounded in data availability, fraud realities, and delivery constraints.
  • Strong attention to detail and a bias toward shipping reliable and measurable capabilities.
  • Communication Skills
  • Able to clearly articulate ML concepts, risks, and tradeoffs to non technical stakeholders.
  • Comfortable supporting customer facing or internal discussions around model behavior and limitations.
  • Able to document requirements and acceptance criteria with precision.

RESPONSIBILITIES

  • ML Feature and Capability Ownership
  • Own and manage the backlog for ML-driven biometric and document verification capabilities.
  • Translate fraud, identity, and customer requirements into clear and actionable ML work items.
  • Partner closely with ML engineers and data scientists to refine problem statements into feasible deliverables.
  • Define acceptance criteria that reflect real world performance, not just offline model metrics.
  • Embedded ML Team Collaboration
  • Serve as the primary product owner for ML and data science teams.
  • Participate actively in model design discussions, prioritization, and tradeoff analysis.
  • Challenge scope, timelines, and modeling approaches when misaligned with business or risk objectives.
  • Propose alternate ideas across data strategy, modeling approaches, workflow design, or deployment patterns.
  • Production Readiness and Lifecycle Support
  • Support model lifecycle activities including training, evaluation, deployment, and retraining.
  • Ensure monitoring, drift detection, and feedback loops are incorporated into delivery plans.
  • Help define rollout, experimentation, and rollback guardrails.
  • Data and Labeling Execution
  • Partner with agent operations and data teams on labeling strategy and data quality.
  • Help define labeling schemas and workflows to support effective model training.
  • Identify risks related to label noise, bias, or insufficient coverage across geographies and document types.
  • Fraud and Adversarial Awareness
  • Incorporate fraud patterns and adversarial thinking into backlog prioritization.
  • Ensure features and models are resilient to evolving attack vectors such as spoofs, deepfakes, and injection attacks.
  • Support layered and defense in depth approaches rather than single model dependency.
  • Cross Functional Coordination
  • Work closely with engineering, fraud, compliance, legal, and customer teams.
  • Support internal and external conversations where ML behavior or performance needs explanation.
  • Translate technical constraints into clear delivery expectations for non technical stakeholders.

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