Senior Staff Privacy Engineer - Moveworks

ServiceNow•Mountain View, CA

About The Position

Moveworks, acquired by ServiceNow in December 2025, provides an agentic AI assistant platform that connects employees with business systems through natural language and automates work. The Senior Staff Privacy Engineer will help ensure sensitive data is handled appropriately across AI products, including how data is collected and used and what protections apply across models, tools, logs, and data pipelines. This is a hands-on Senior Staff individual contributor role. The engineer will write production code and set technical direction across teams, partnering with Privacy, ML, Security, Product, and Infrastructure teams from design through production. The role focuses on improving sensitive-data detection and masking, assessing privacy risks in ML systems, implementing customer data-use and retention controls, and building reusable protections and engineering standards. Success in the first year includes selecting and deploying ML models for priority privacy use cases; integrating shared masking and data-protection components into priority product areas; running repeatable privacy evaluations during model and product releases and shipping fixes based on findings; and implementing and monitoring priority data-use, retention, and deletion requirements in production.

Requirements

Typically 7 or more years building production software, including substantial experience in privacy engineering and applied ML.
Strong Python skills and experience with Go, Java, C++, or another production systems language.
Experience shipping, testing, and operating production software.
Hands-on experience evaluating, selecting, and deploying ML models, including dataset curation, model adaptation or fine-tuning, error analysis, and model serving.
Experience building sensitive-data detection and masking systems, including managing accuracy, performance, and integration tradeoffs.
Experience assessing privacy risks in ML and LLM systems and implementing mitigations.
Ability to explain what tests establish and where uncertainty remains.
Deep understanding of data minimization, pseudonymization, anonymization, and re-identification risk, including the limitations of removing direct identifiers.
Experience building data-protection and lifecycle controls in distributed cloud systems.
A record of taking ambiguous engineering problems from design through delivery, setting technical direction across teams, and mentoring engineers.

Nice To Haves

Experience applying differential privacy or other privacy-enhancing technologies, including evaluating assumptions and privacy-utility tradeoffs.
Privacy engineering experience with agentic systems, retrieval-augmented generation, embeddings, or synthetic datasets.
Experience in enterprise, multi-tenant, or regulated environments and translating privacy requirements such as GDPR and CCPA into technical controls.

Responsibilities

Assess, benchmark, and select ML models and techniques that meet privacy requirements, using representative evaluation datasets to compare detection coverage, false positives, latency, cost, and product-quality impact.
Own the implementation of privacy-focused ML models in products, from prototype through production; train or fine-tune models when needed, build supporting services and APIs, and optimize inference performance and reliability.
Build and deploy sensitive-data detection, masking, redaction, and tokenization for structured and unstructured data across logs, traces, APIs, analytics, and annotation tools.
Implement consistent controls governing when data is masked and who can access unmasked content.
Build privacy evaluations for ML models and surrounding systems, including assessments of memorization, training-data extraction, membership inference, and sensitive-data disclosure through retrieval or tool use.
Implement mitigations and regression tests in model and product release workflows.
Write and maintain code enforcing customer requirements for data use, access, retention, and deletion across services and data stores, distinguishing permitted inference, evaluation, annotation, and training.
Prototype and implement privacy-preserving approaches to evaluation and synthetic data; measure privacy risk and utility, validate assumptions, and integrate suitable solutions into ML pipelines.
Conduct privacy design reviews with engineering teams, identify gaps in proposed data flows and architectures, and own or partner on code changes to address them.
Build reusable components and engineering standards to help teams apply privacy protections consistently.
Monitor privacy controls and models in production, investigate failures, and deliver improvements.
Use error analysis and production feedback to improve detection quality, address regressions, and resolve customer-reported privacy issues.
Set technical direction across teams and mentor engineers.