Top 10 highest paying companies that use computer vision

$150-200k + equity

Voxel is pushing the boundaries of applied computer vision to solve risk in physical industries. Accidents and injuries are bad for people and bad for business. Help us ensure the people who make our world go 'round get home safe, every day. Our technology addresses the key cost drivers for workers’ compensation, general liability, and property damage, which cost employers over $500 billion annually. We have a wonderful engineering team you'll love working with and plenty of complex problems to sink your teeth into. Plus, fill your resume with trendy buzzwords by joining Voxel: AI, computer vision, the cloud, Kubernetes, distributed systems, edge computing, React, and so much more! Job descriptions + apply here: https://jobs.lever.co/Voxel/ Or email me directly: troy@

$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.

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.