Engineering Manager, Identification Accuracy

Accountabilities:
As an Engineering Manager, you will lead a specialized team responsible for improving identification accuracy through advanced machine learning solutions. You will balance people leadership, technical direction, and cross-functional collaboration to deliver reliable, scalable, and high-impact products. Lead and develop a multidisciplinary team of ML Engineers, Data Scientists, Analysts, and Analytics Engineers, fostering collaboration, psychological safety, and technical excellence. Own and drive the team roadmap in partnership with engineering leadership and cross-functional stakeholders, ensuring priorities align with business and customer needs. Support the development, evaluation, deployment, and continuous improvement of machine learning models that enhance identification accuracy at scale. Guide teams in building reliable production ML systems, including data pipelines, feature engineering processes, model training workflows, and deployment practices. Establish a culture of continuous improvement, experimentation, and data-driven decision-making. Collaborate with engineering, product, and customer-facing teams to translate customer challenges into technical priorities. Communicate model performance, technical tradeoffs, and roadmap decisions clearly to both technical teams and business stakeholders. Help create an environment where team members can grow professionally through coaching, feedback, and career development. Requirements: The ideal candidate is an experienced engineering leader with a strong background in machine learning, software engineering, or data-driven products. You should have proven experience managing technical teams and delivering production systems in fast-moving environments. Minimum of 2 years of experience leading ML, data science, or engineering teams in an agile and rapidly evolving environment. 5+ years of professional experience in software engineering, machine learning, data science, or a related technical field. Demonstrated experience leading teams that build and operate production machine learning systems. Strong understanding of ML development workflows, including data pipelines, feature engineering, model training, evaluation, and deployment. Proven ability to build, mentor, and develop high-performing multidisciplinary teams. Excellent communication skills with the ability to explain complex technical concepts, model behavior, and data challenges to diverse audiences. Experience driving results in scaling environments where priorities shift and ambiguity is common. Strong collaboration skills and the ability to work effectively with engineering, product, and business teams. Preferred Qualifications: Experience managing teams working with large-scale behavioral, event, or customer data. Familiarity with ML infrastructure and MLOps tools such as experiment tracking platforms, feature stores, model registries, and ML CI/CD pipelines. Experience in fraud prevention, identity verification, trust and safety, or related security domains. Hands-on experience with analytics engineering tools such as dbt or similar technologies. Experience collaborating with platform and API engineering teams on performance, scalability, and reliability requirements. Benefits: Fully remote work environment with flexibility to work from your preferred location. Opportunity to lead impactful projects in machine learning, fraud prevention, and digital security. Competitive compensation package based on experience, location, and market conditions. Opportunity to work with a globally distributed team of talented engineers and data professionals. Strong focus on professional growth, learning, and career development. Inclusive and collaborative culture that values diverse perspectives and backgrounds. Ability to contribute to innovative solutions that improve online trust and security at scale.

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.
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