Engineering Manager, Identification Accuracy

Accountabilities

• Lead and grow a multidisciplinary Identification Accuracy team spanning ML engineers, data scientists, analysts, and analytics engineers, fostering psychological safety, technical excellence, accountability, and continuous improvement.
• Own the team’s technical roadmap in collaboration with senior engineering leadership and cross-functional stakeholders, identifying opportunities to improve model quality and address complex identification challenges.
• Drive measurable model accuracy outcomes by enabling the team to design, train, evaluate, and deploy machine learning models that improve identification performance across billions of devices.
• Oversee the delivery of production ML systems across data pipelines, feature engineering, model development, evaluation, and deployment, ensuring reliability and scalability.
• Partner closely with platform and API engineering teams to understand downstream requirements, performance expectations, and latency constraints.
• Collaborate with Product and customer-facing teams to translate customer needs and business priorities into technical initiatives and product improvements.
• Communicate model performance, data-quality considerations, technical trade-offs, risks, and roadmap priorities clearly to both technical teams and senior business stakeholders.
• Build a high-performing, multidisciplinary organization by mentoring team members, developing technical leaders, and creating an environment where people can do their best work.
• Continuously improve engineering and ML practices, including experimentation, model evaluation, MLOps, data workflows, and operational processes.

Requirements

• 5+ years of professional experience in software engineering, machine learning, data science, or a related technical discipline, including at least 2 years leading an ML or data science team in a fast-paced environment.
• Proven experience managing technical teams that deliver production machine learning systems, from data pipelines and feature engineering through model training, evaluation, and deployment.
• Demonstrated success building and developing high-performing multidisciplinary teams that include engineers, data scientists, analysts, or analytics engineers.
• Strong technical understanding of machine learning and data systems, with familiarity with MLOps practices and tooling such as experiment tracking, feature stores, model registries, and ML CI/CD pipelines.
• Experience working with large-scale behavioral or event data in production environments.
• Hands-on familiarity with data stack and analytics engineering technologies such as dbt or similar tools.
• Ability to work effectively with platform and API engineering teams and understand technical requirements, system dependencies, and latency constraints.
• Excellent written and verbal communication skills, with the ability to translate complex model behavior, data-quality challenges, and technical trade-offs for both technical and non-technical audiences.
• Demonstrated ability to deliver results in rapidly scaling environments where priorities evolve and ambiguity is part of the work.
• Strong people leadership skills, including coaching, mentoring, team development, and fostering a culture of psychological safety and high performance.
• Experience in fraud detection, identity, trust & safety, or a related domain is a plus, but not required.
• Must be authorized to work from Poland; visa sponsorship is not available for this role.

Benefits

• Competitive compensation package; for US-based employees, the stated cash compensation range is $159,000–$215,000 USD, while compensation for Poland and other locations may vary according to local market benchmarks.
• Fully remote work environment with a globally distributed team.
• Opportunity to lead a multidisciplinary ML and data organization solving challenging problems at significant scale.
• Exposure to cutting-edge machine learning, fraud detection, identity, and data technologies.
• High level of autonomy and meaningful influence over technical strategy, team development, and product outcomes.
• Inclusive environment that values diverse experiences, perspectives, and backgrounds.
• Opportunity to work on technology used by major enterprises and high-growth companies worldwide.