Engineering Manager, Identification Accuracy

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engineering Manager, Identification Accuracy based in United States.

This is a high-impact engineering leadership role at the intersection of machine learning, data science, and fraud prevention. You will lead a multidisciplinary team responsible for improving the accuracy and reliability of a critical identification platform. The role combines people leadership, technical strategy, and hands-on program direction in a globally distributed, fully remote environment. You’ll shape the team roadmap and guide the development of production ML systems operating at massive scale. Working closely with engineering, product, and customer-facing teams, you’ll translate business needs into meaningful technical priorities. This is an opportunity to influence both the technology and the people behind a best-in-class fraud detection capability.

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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.