Senior Staff Privacy Engineer - Moveworks

ServiceNow•Mountain View, CA

14h

About The Position

Moveworks, part of ServiceNow, is seeking a hands-on Senior Staff Privacy Engineer to shape the architecture and protections that ensure sensitive data is handled appropriately across AI products and workflows. The role spans data collection and use, model and tool interactions, logs, and data pipelines. As a senior individual contributor, you will write production code and set technical direction across teams while partnering with Privacy, ML, Security, Product, and Infrastructure from design through production. The engineer will evaluate and deploy ML models for privacy use cases; build sensitive-data detection, masking, redaction, and tokenization; assess privacy risks in ML and LLM systems; implement customer data-use, access, retention, and deletion controls; and develop privacy-preserving evaluation and synthetic-data approaches. The role also includes design reviews, reusable engineering components and standards, production monitoring, investigation of failures, and continuous improvement based on error analysis and customer feedback. Success in the first year includes deploying ML models for priority privacy use cases with measured quality and acceptable latency and cost; integrating shared masking and protection components into priority product surfaces; establishing repeatable privacy evaluations in release workflows; 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 limits of removing direct identifiers.
Experience building data-protection and lifecycle controls in distributed cloud systems.
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 for agentic systems, retrieval-augmented generation, embeddings, or synthetic datasets.
Experience in enterprise, multi-tenant, or regulated environments and translating requirements such as GDPR and CCPA into technical controls.

Responsibilities

Assess, benchmark, and select ML models and techniques that meet privacy requirements; build representative evaluation datasets and 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, including training or fine-tuning where needed, building services and APIs, and optimizing 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 evaluations for memorization, training-data extraction, membership inference, and sensitive-data disclosure through retrieval or tool use.
Implement mitigations and add regression tests to 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 that help teams apply privacy protections consistently.
Monitor production privacy controls and models, investigate failures, and ship 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.