filters testing at Weekday AI

, , India -

Full Time

Start Date

Immediate

Expiry Date

16 Mar, 26

Salary

0.0

Posted On

16 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Quality Assurance, People Leadership, Testing Strategy, Model Performance, Automated Testing, Validation, A/B Testing, Bias Detection, System Design, Collaboration, Mentoring, Communication, Ownership Mindset, Model Monitoring, Continuous Improvement

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients JobType: full-time As 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. 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

Responsibilities

Lead the design, testing, validation, and continuous improvement of ML-driven filtering systems. Manage and mentor engineering teams while collaborating with various stakeholders to ensure the robustness and scalability of automated filters.