Privacy Program Engineer
Remote · 4-7 years experience · Full-time · Remote · Posted Oct 8, 2026
At a glance
- Experience
- 4-7 years experience
- Employment
- Full-time
- Workplace
- Remote
- Location
- Remote
About Fireworks AI
- Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.
About the role
- We're looking for a Privacy Program Engineer to own the technical core of our privacy program.
- This is a privacy role with real technical depth - you'll write scripts, queries, and automation.
- You'll report to our Privacy Lead and be a key owner of large portions of our privacy program, as well as the key technical expert on the privacy team.
Responsibilities
- Build the privacy automation layer: scripts, scheduled jobs, API integrations, and LLM-based agents that handle request routing, evidence collection, assessment intake, and control monitoring.
- Own the data subject rights (DSR) program and the system behind it: build the intake, identity verification, fan-out across data stores, and fulfillment tracking as an automated pipeline rather than a checklist, with the audit trail generated as a byproduct.
- Build and maintain a data inventory and ROPA that derives from the environment rather than from interviews: pull from cloud APIs, warehouse metadata, IaC, and service catalogs so the map updates when the systems do, and reconcile drift.
- Embed privacy into how we build: review designs and PRs for data handling, advise on de-identification, pseudonymization, tokenization, field-level encryption, and access scoping, and give engineers a concrete pattern to use rather than a policy to read.
- Own retention and deletion as an engineering problem: translate retention requirements into concrete rules per data store, work with engineering on enforcement in pipelines and backups, and build the verification that proves deletion actually happened.
- Support consent management and preference handling across our web properties and product surfaces.
- Solve AI data problems directly: trace how customer data moves through training, fine-tuning, inference, and logging; validate zero-retention and isolation claims against what the platform actually does; and build the checks that keep those claims true as the platform changes.
- Take on additional privacy projects as the program evolves; we're a growing team with dynamic priorities.
Requirements
- 4–7 years of experience in privacy, GRC, IT audit, information security, or a closely related field, with meaningful hands-on privacy work.
- Working knowledge of GDPR and CCPA/CPRA, and familiarity with ISO 27701, ISO 27001, ISO 42001, SOC 2, and NIST.
- Hands-on experience with technical privacy operations: data subject requests, ROPA or data inventory maintenance, privacy impact assessments, or retention enforcement.
- Evidence that you automate your own work - AI tooling, scripts, workflow builders, or aggressive use of a privacy/GRC platform's automation features.
- Working proficiency in SQL and at least one scripting language - enough to query a warehouse, call an API, parse a schema, and automate a recurring task without help.
- Hands-on familiarity with cloud environments (AWS, GCP, or Azure): IAM and access scoping, logging, data stores and their retention behavior, and how to find where data actually lives and how it’s used.
- Strong written communication; you can translate privacy requirements into language engineers, customers, and non-technical employees understand.
Preferred qualifications
- Built something with an LLM API or agent framework that other people relied on.
- Experience with de-identification or synthetic data techniques.
- Exposure to data lineage or catalog tooling (dbt, DataHub, Atlan, Monte Carlo, OpenMetadata).
- Worked on a privacy or security problem at an AI/ML company specifically - model data flows, inference logging, training data provenance.
- Startup or fast-growing SaaS background.
Workplace
- Remote
Skills
- SQL
- AWS
- GCP
- Azure
- Dbt
- DataHub
- Atlan
- Monte Carlo
- OpenMetadata
