Senior Staff Privacy Engineer - Moveworks

ServiceNow•Mountain View, CA

About The Position

Moveworks, part of ServiceNow, is hiring a hands-on Senior Staff Privacy Engineer to shape privacy architecture and protections for AI products that handle sensitive customer data. The role works across the data lifecycle, from collection and use to controls applied to models, tools, logs, and data pipelines. It is a senior individual-contributor position that sets technical direction across teams and includes writing production code. The engineer will work with Privacy, ML, Security, Product, and Infrastructure teams from design through production. The work includes evaluating and deploying ML models for privacy use cases; building sensitive-data detection, masking, redaction, and tokenization; assessing privacy risks in ML and LLM systems; implementing customer data-use, access, retention, and deletion controls; and developing privacy-preserving evaluation and synthetic-data approaches. The role also includes conducting design reviews, monitoring production controls and models, investigating failures, and improving protections based on error analysis and customer feedback. In the first year, success means deploying selected models for priority privacy use cases with measured improvements in detection quality and acceptable latency and cost; integrating shared masking and data-protection components into priority product surfaces; running repeatable privacy evaluations in release workflows 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 understanding 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.
Experience translating privacy 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 implementation of privacy models in products from prototype through production, including training or fine-tuning when 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 for when data is masked and who can access unmasked content.
Build privacy evaluations for ML models and surrounding systems, including tests for memorization, training-data extraction, membership inference, and sensitive-data disclosure through retrieval or tool use.
Implement privacy 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, identify gaps in 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 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.