Fireworks AI
From Fireworks AI's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
ABOUT THE ROLE We're looking for a Privacy Program Engineer to own the technical core of our privacy program.
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.
We're looking for a Privacy Program Engineer to own the technical core of our privacy program. We've achieved ISO 27001, ISO 27701, ISO 42001, and SOC 2 Type II, and we run active GDPR and CCPA/CPRA programs - now we need someone to run the machinery that keeps all of it working, and to make that machinery largely run itself.
This is a privacy role with real technical depth - you'll write scripts, queries, and automation. From day one you'll own real workstreams: data subject requests, the data inventory and ROPA, hands-on privacy reviews for new vendors and features.. 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.
This is not a ticket queue with a title. We're a small team at a company that builds AI infrastructure, and we intend to leverage it - you'll own the direction and design of the technical systems, automations, and tools that help us meet our privacy obligations at Fireworks.
WHO THIS ROLE IS FOR
You build systems, not queues. You've used AI tooling, automation, scripting, or workflow platforms to eliminate manual privacy work.
You're technical enough to be dangerous. You read code to understand what a service actually does with data. You can query a database without asking for help. You've automated something real with code, a workflow engine, or an LLM-based agent and you know how to build scalable tools running against production systems and data.
You take a project and run with it. Given a rough problem and a deadline, you come back with it done. You’re not afraid to make tactical decisions and judgement calls.
You take extreme ownership. You're comfortable owning something outright and figuring it out - your workstreams are yours. You surface problems early, fix them, and make sure they don't recur.
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.
HOW THE ROLE WILL GROW
Process ownership - move from operating established processes to owning entire workstreams end to end.
Automation ownership - take the lead on privacy tooling and agent design as the program's automation surface grows.
Advisory depth - advise product and engineering teams directly on technical privacy-by-design decisions.
Growing influence - represent privacy in cross-functional projects, be the go-to technical privacy SME, and help shape where the program goes next.
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. Tell us what you built and what it replaced.
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.
NICE TO HAVE
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.
Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
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