This role is for one of the Weekday's clients
JobType: full-timeAs an
Engineering Manager - Machine Learning (Filters Testing)
, you will lead the design, testing, validation, and continuous improvement of large-scale ML-driven filtering systems that power high-impact products. This role sits at the intersection of machine learning engineering, quality assurance, and people leadership, with a strong focus on ensuring robustness, fairness, accuracy, and scalability of automated filters. You will manage and mentor high-performing engineering teams while partnering closely with product, data science, trust & safety, and infrastructure stakeholders to deliver reliable ML systems in a fast-paced, production-first environment.
Requirements
Key Responsibilities
- Lead end-to-end testing strategy for ML-based filtering systems, including content, relevance, fraud, or safety filters
- Define quality metrics, evaluation frameworks, and acceptance criteria for ML model performance in production
- Drive development of automated testing pipelines for ML models, features, and data flows
- Oversee offline and online validation, A/B testing, shadow deployments, and regression testing of ML filters
- Partner with data science teams to translate model behavior into testable, measurable outcomes
- Ensure coverage for edge cases, bias detection, adversarial inputs, and failure scenarios
- Collaborate with platform and infra teams to scale testing systems across high-throughput environments
- Review system design, testing architecture, and production-readiness of ML pipelines
- Establish best practices for model monitoring, alerting, rollback, and continuous improvement
- Lead cross-functional reviews on model quality, risk, and compliance readiness
- Balance speed and quality while delivering reliable ML systems at scale
- Own technical execution while aligning testing priorities with business goals
- Build, manage, and mentor engineering teams with a strong culture of ownership and excellence
What Makes You a Great Fit
- Strong experience as an Engineering Manager leading ML-focused engineering teams
- Deep understanding of machine learning systems, model lifecycle, and production deployment
- Proven background in designing testing frameworks for ML models and data-driven systems
- Hands-on exposure to evaluation metrics, model validation, and experimentation methodologies
- Ability to reason about ML failure modes, bias, fairness, and robustness
- Experience working with large-scale data pipelines and real-time or batch inference systems
- Strong system design skills with a focus on scalability, reliability, and observability
- Comfortable collaborating across product, data science, trust & safety, and infrastructure teams
- Demonstrated people leadership with the ability to coach, mentor, and grow senior engineers
- Excellent communication skills to influence technical and non-technical stakeholders
- Bias for action, ownership mindset, and ability to operate in ambiguity
- Passion for building high-quality, responsible ML systems that impact users at scale
