Engineering Manager, Identification Accuracy

What You'll Do

• Lead and grow the Identification Accuracy team — a multidisciplinary group of ML Engineers, Data Scientists, Analysts, and Analytics Engineers — fostering psychological safety, technical excellence, and a culture of continuous improvement.
• Own the team's roadmap in close partnership with senior engineering leadership and cross-functional stakeholders, driving innovative solutions to identification-specific challenges and continuously raising the bar on model quality.
• Drive model accuracy outcomes by enabling your team to design, train, evaluate, and ship ML models that improve identification accuracy at scale across billions of devices.
• Build bridges across the organization — partnering closely with the Identification Engineering team (who operates the API your models power) as well as Product, and customer-facing teams to translate customer needs into technical priorities.
• Communicate effectively across technical and non-technical audiences, translating model performance and roadmap tradeoffs into language that resonates with business stakeholders and executive leadership.

Requirements

• Minimum of 2 years of experience in a leadership role in a ML or data science team in an agile, fast-paced environment.
• At least 5 years of professional experience in software engineering, machine learning, or a related technical discipline.
• Demonstrated ability to lead technical teams that ship production ML systems — from data pipelines and feature engineering through model training, evaluation, and deployment.
• Proven track record of building and developing high-performing, multidisciplinary teams including engineers, data scientists, and/or analysts.
• Strong communication skills with the ability to translate complex model behavior, data quality issues, and technical tradeoffs to both technical teammates and non-technical stakeholders.
• Demonstrated success driving outcomes in fast-moving, scaling environments where priorities evolve and ambiguity is the norm.

Preferred Qualifications

• Experience managing teams that work with large-scale behavioral or event data in a production setting.
• Familiarity with ML infrastructure and MLOps tooling — experiment tracking (e.g., MLflow), feature stores, model registries, and CI/CD pipelines for ML.
• Background in fraud detection, identity, or trust & safety domains is a plus but not required.
• Hands-on experience with data stack technologies such as dbt or similar analytics engineering tooling.
• Comfort working closely with platform and API engineering teams to understand downstream requirements and latency constraints.