Location: NYC HQ or Remote (U.S.)| Full-time | Engineering
Who we are.Reality Defender is an award-winning cybersecurity company helping enterprises and governments detect deepfakes and AI-generated media. Utilizing a patented multi-model approach, Reality Defender is robust against the bleeding edge of generative platforms producing video, audio, imagery, and text media. Reality Defender's API-first deepfake detection platform empowers teams and developers alike to identify fraud, disinformation campaigns, and harmful deepfakes in real time.
Backed by world class investors including DCVC, Illuminate Financial, Y Combinator, Booz Allen Hamilton, IBM, Accenture, Rackhouse, and Argon VC, Reality Defender works with leading enterprise clients, financial institutions, and governments in order to ensure AI-generated media is not used for malicious purposes.
Youtube: Reality Defender Wins RSA Most Innovative Startup
The Staff Data Platform Engineer Role.Reality Defender's detection models are only as strong as the data behind them, and every new generative tool raises the bar on the volume and variety of media we need to train and evaluate against. The Staff Data Platform Engineer will own the shared AI data platform behind our vision, audio, and multimodal research, turning acquired and internally generated image, video, audio, and text data into reliable, reproducible datasets, from continuous ingestion through processing, governance, and export. This is a senior, hands-on role where you'll complete our Databricks and distributed processing platform and build the reusable infrastructure behind our in-house synthetic data generation, including producing the very deepfakes our models learn to catch. This is a full-time role, reporting to our Director of Data. Responsibilities include:
Own the end-to-end data platform for AI research: continuous ingestion, preprocessing, governance, and export of image, video, audio, and text data at high volume.
Complete and scale our Databricks lakehouse and distributed processing stack (Spark, Ray, or similar), making architecture and build-vs-buy decisions that hold up as data volume grows.
Build reusable infrastructure for in-house synthetic data generation, so researchers can produce new deepfake variants across modalities on demand instead of one-off.
Make every dataset reproducible and traceable through versioning, lineage, metadata, quality checks, and stable train/eval splits, so any model result can be tied back to the exact data behind it.
Own pipeline reliability, observability, and processing cost per dataset as core metrics of the platform.
Partner with ML researchers to understand their data needs and turn them into self-serve tools that speed up model iteration.
Mentor engineers and raise the bar on data engineering practices: code review, testing, documentation, and runbooks.
Work with Security and compliance stakeholders to make sure data handling, licensing, and access controls meet the standards our enterprise and government clients expect.
Help shape the long-term data platform roadmap with engineering and research leadership.
8+ years building data platforms or data infrastructure, including staff- or lead-level ownership of production pipelines that generate, ingest, or deliver data at high volume.
Deep hands-on experience with distributed processing (Spark, Ray, or similar) and lakehouse platforms, ideally Databricks, on top of cloud object storage (S3, GCS, or Azure Blob).
Production experience with Databricks, Unity Catalog, Delta Lake, and Spark on AWS, including performance tuning, access controls, and operational troubleshooting.
Experience working with large unstructured media such as video, audio, or images, including the realities of large files, decoding and transcoding, and metadata at scale.
A track record of building reproducible ML datasets: versioning, lineage, data quality validation, and orchestration (Airflow, Dagster, Databricks Workflows, or similar).
Strong Python and SQL, and the judgment to own ambiguous platform problems end-to-end on a lean team.
Own infrastructure and deployment automation using Terraform and CI/CD, including AWS storage, IAM, compute, Databricks resources, and distributed processing environments.
Experience building synthetic data generation pipelines or running generative models (diffusion, voice cloning/TTS, video generation) at scale. (nice to have)
Background supporting ML research teams with training and evaluation data, especially for multimodal models. (nice to have)
Experience in security, fraud, trust & safety, or another adversarial domain, or at a Series A or B startup where scope regularly outpaces headcount. (nice to have)
Own a mature, self-serve data platform that researchers use to create, version, and export datasets without waiting on platform support.
Have scaled synthetic data generation across vision, audio, and multimodal research, broadening our detection coverage of new generative techniques.
Deliver reproducible evaluation datasets that let the company compare model versions with confidence.
Be setting the multi-year data platform roadmap and leading high-leverage initiatives beyond the original scope.
Reality Defender offers the following benefits to all our employees, regardless of location:
Healthcare plans with 100% premium coverage for employees and partial coverage available for dependents
Dental and Vision plans with 100% premium coverage for employees and their dependents
Short/Long-term disability and life insurance plans with 100% premium coverage for employees
FSA/HSA and 401k programs
Equity compensation
20 days of PTO per year
12 weeks of Parental Leave
Learning and Development budget
Monthly wellness benefits
Annual company-sponsored offsite
For employees working from Reality Defender's HQ in NYC, we offer the following benefits:
Daily in-office lunch through UberEats
Commuter benefits
Remote Fridays
Happy Hours and other local events
Base salary range: 210,000 - 260,000, based on experience, level, and location. Role includes equity.
Skills Required
- 8+ years building data platforms or data infrastructure, including staff- or lead-level ownership of high-volume production pipelines
- Deep hands-on experience with distributed processing such as Spark or Ray and lakehouse platforms, ideally Databricks, using cloud object storage
- Production experience with Databricks, Unity Catalog, Delta Lake, and Spark on AWS, including performance tuning, access controls, and operational troubleshooting
- Experience working with large unstructured media such as video, audio, or images, including large files, decoding, transcoding, and metadata at scale
- Experience building reproducible ML datasets using versioning, lineage, data quality validation, and orchestration tools such as Airflow, Dagster, or Databricks Workflows
- Strong Python and SQL skills and the judgment to own ambiguous platform problems end-to-end
- Experience owning infrastructure and deployment automation using Terraform and CI/CD, including AWS storage, IAM, compute, Databricks resources, and distributed processing environments
- Experience building synthetic data generation pipelines or running generative models such as diffusion, voice cloning, text-to-speech, or video generation at scale
- Background supporting ML research teams with training and evaluation data, especially for multimodal models
- Experience in security, fraud, trust and safety, another adversarial domain, or a Series A or B startup
What We Do
Stop deepfakes before they become a problem with Reality Defender’s proactive AI-generated media detection platform. Detect dangerous AI-generated and manipulated content across audio, video, images, and text with our enterprise-grade API and web app.
