Roles
Compensation
USD 115000 - 140000
$115,000 - $140,000 a year, plus healthcare benefits (wellness), retirement/pension contributions and MTK stock plan participation, life and disability coverage, paid time off, e-learning license and tuition reimbursement, hackathons, home office setup allowance, and optional benefits like pet insurance, identity theft protection, and legal assistance.
- Salary period
- yearly
Benefits
- Universal, supplemental, and private healthcare plan choices based on country specifics
- Retirement/pension plan contributions
- MTK stock plan participation
- Life event & disability coverage
- Generous annual leave
- Company holidays
- Volunteer time off
- E-learning license
- Tuition reimbursement
- Hackathons
- Home office setup allowance
- Pet insurance (optional)
- Identity theft protection (optional)
- Legal assistance (optional)
Tech stack
Required
Nice to have
Location
Anywhere in the World
Work setup
- Employment
- full-time
- Level
- Mid-level
- Remote policy
- Remote; 100% remote-friendly with optional work from the San Diego, CA office.
- Remote scope
- worldwide
Role details
Responsibilities
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Requirements
- 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.
- 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.
- Comfortable evaluating tradeoffs between different modeling and data approaches.
- 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.
- 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.
Application
To apply: https://weworkremotely.com/remote-jobs/mitek-systems-product-owner-machine-learning. Mitek may use AI tools to support parts of the hiring process (reviewing applications, analyzing resumes, assessing responses); final decisions are made by humans.
- Portfolio
- not required
- GitHub
- not required
- Cover letter
- unclear
- Apply flow
- external
Company context
- Product
- Digital & biometric identity authentication, fraud prevention, and mobile deposit solutions; verified identity platform and image capture solutions
- Industry
- Digital Identity and Fraud Prevention
- HQ
- San Diego, California, United States
Description
Mitek (NASDAQ: MITK) is a global leader in digital & biometric identity authentication, fraud prevention, and mobile deposit solutions. The company is seeking a Product Owner to drive execution of machine learning based capabilities across biometric authentication and document verification. This is a hands-on delivery role embedded with machine learning, engineering, and fraud teams, responsible for defining initiatives, managing the ML backlog, translating requirements into actionable work items, defining acceptance criteria aligned to real-world performance, and supporting production readiness across the model lifecycle. Essential Responsibilities: - 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 with ML engineers and data scientists to refine problem statements into feasible deliverables. - Define acceptance criteria reflecting real world performance. - Serve as the primary product owner for ML and data science teams. - Participate in model design discussions, prioritization, and tradeoff analysis; challenge scope/timelines when misaligned. - Propose alternate ideas across data strategy, modeling approaches, workflow design, or deployment patterns. - Support model lifecycle activities including training, evaluation, deployment, and retraining. - Ensure monitoring, drift detection, and feedback loops are incorporated into delivery plans; define rollout, experimentation, and rollback guardrails. - Partner with agent operations and data teams on labeling strategy and data quality; define labeling schemas and workflows. - Identify risks related to label noise, bias, or insufficient coverage across geographies and document types. - Incorporate fraud patterns and adversarial thinking; ensure resilience to spoofs, deepfakes, injection attacks. - Support layered/defense-in-depth approaches rather than single model dependency. - Coordinate with engineering, fraud, compliance, legal, and customer teams; translate technical constraints into delivery expectations for non-technical stakeholders. Required Knowledge, Skills & Abilities: - 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. - 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 biometric and document identity space (highly valuable). - Ability to evaluate tradeoffs between modeling and data approaches. - Comfortable challenging/providing alternatives when solutions are over-engineered or not production ready. - Strong attention to detail and bias toward shipping reliable and measurable capabilities. - Ability to articulate ML concepts, risks, and tradeoffs to non-technical stakeholders. Preferred Experience: - Hands-on experience shipping biometric or document-based ML solutions into production. - Experience in fraud/identity/regualted environments such as financial services or fintech. - Familiarity with model monitoring, drift management, and feedback loops. - Experience working with global document types and jurisdictional variation.
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