Senior Technical Program Manager Data Labeling, Data and Machine Learning

As a Senior Technical Program Manager: Data Labeling at Material Security, you'll be part of a team of experienced, world-class engineers, working to protect our users and their privacy (e.g., inboxes from breaches, targeted phishing, fraud, and lateral account takeover). Your mission is to develop high quality data sets that will be used to create ML/AI models that detect security relevant data and behavior (phishing emails, sensitive data in email and drive).

Responsibilities

You will lead cross-functional initiatives to build, scale, and optimize data annotation programs critical to AI model performance.

You'll own program delivery across internal teams, vendor partners, and ML stakeholders to ensure high-quality labeled datasets are delivered on time and at scale.

This role is both strategic and execution-driven: you'll define roadmaps, manage SLAs, create scalable processes, and resolve bottlenecks to ensure the labeling engine is efficient, quality-controlled, and model-aligned.

• Define and drive end-to-end execution of large-scale annotation programs across multiple data types.

• Collaborate with ML, product, and data operations teams to scope and prioritize labeling needs.

• Own vendor engagement: onboarding, SLA management, training, and quality reviews.

• Build feedback loops between annotators and model performance to inform labeling strategies.

• Create dashboards and reporting mechanisms to track labeling velocity, quality, and cost.

• Lead initiatives to improve labeling efficiency through tooling enhancements and process automation.

• Be the voice of labeling in cross-functional forums-translating model needs into operational plans.

• Manage and mentor a team of trained threat analysts who conduct our labeling.

• Conduct analysis of the quality of the labeling and for insights into how our detections can be improved.

• Hire and train new or replacement threat analysts

What We’re Looking For

Must Haves

• 5+ years of program management experience, ideally in ML ops, data labeling, or AI infrastructure.

• Proven track record building and managing remote labeling teams.

• Strong understanding of ML lifecycle stages and the importance of annotated data quality.

• Experience defining SOPs, audit mechanisms, and workflows for scalable data labeling.

• Proficient in project management tools such as Jira, Asana, or Linear for program tracking

• A deep understanding on ML Operations labelling tools and experience building or maintaining an annotation tool.

• Strong analytical and communication skills; ability to synthesise feedback from ML, ops, and product stakeholders and also analyzed data to spot trends in our labeling or detection quality.

• Understanding of data privacy and security standards and how they can be followed in a labeling program.

Nice to Haves

• Exposure to LLMs, foundation models, or active learning-based data curation.

• Familiarity with annotation for multimodal inputs (e.g., Image, Text, Documents, OCR based forms etc)

• Knowledge of quality scoring frameworks, inter-annotator agreement (IAA), or QA loop design.

• The ability to develop and maintain labeling quality metrics and analytic insights and report on those to senior management

• Technical background (e.g., in ML, data science, or engineering) is a plus.

Material Security is a remote-first workplace with an office in San Francisco, California.

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Compensation at Material Security is determined by a range of factors, including but not limited to the individual’s particular combination of knowledge, skills, competencies, and experience. The projected compensation range for this position is $190,000 - $225,000.

Equal Opportunity Employer Statement

Material Security is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, creed, national origin, ancestry, sex, gender, gender identity or expression, sexual orientation, age, marital status, veteran status, disability, genetic information, or any other legally protected status. All employment decisions are based on qualifications, merit, and business needs.