Research Scientist II – Deepfake Detection & Authentication (Video & Audio)

Pindrop

10d•$160,000 - $185,000•Remote

About The Position

As a Research Scientist II on Pindrop’s core Research team, you will independently lead research initiatives, deliver reproducible experimental results, and help translate machine learning models into real-world product solutions across deepfake video, deepfake audio, and voice and identity authentication. The role focuses on developing and improving synthetic media detection, multimodal deepfake identification, and real-time identity authentication technologies. You will design and evaluate deep learning algorithms, maintain research training and benchmarking pipelines, investigate model behavior and detection gaps, and collaborate with research and engineering teams to transition prototypes into production-ready systems across cloud and edge platforms. You will also contribute to research and code reviews, patents, and peer-reviewed publications. The position calls for scientific rigor, individual ownership, clear communication across technical and non-technical teams, and careful attention to bias, model drift, deployment constraints, documentation, and reproducibility. Pindrop describes the role as remote-first, with regular team on-sites, company-wide events, and other in-person gatherings. The US base pay range is $160,000–$185,000 USD.

Requirements

Master’s degree or PhD in Computer Science, Artificial Intelligence, Electrical Engineering, Mathematics, or a related quantitative field, or equivalent industry research experience.
At least 3 years of professional or post-graduate deep learning research experience, with a dedicated focus in at least one of computer vision, speech/audio processing, or voice biometrics and identity authentication.
Hands-on research experience in audio-visual deepfake detection, synthetic media identification, or multimodal biometric verification.
Strong Python programming proficiency and demonstrated ability to build, maintain, and structure reproducible research tools, experimentation frameworks, or evaluation workflows.
Hands-on mastery of modern machine learning frameworks such as PyTorch, TensorFlow, or Keras.
A record of successful, timely project delivery and translating research findings into practical improvements, supported by peer-reviewed publications or patent contributions.
Ability to conduct rigorous experimental iterations, make data-grounded technical decisions, and communicate complex AI/ML and biometric concepts clearly.
Awareness of bias, drift, and operational deployment constraints when designing, training, and evaluating machine learning models.

Nice To Haves

Functional programming experience in C/C++, or familiarity with model optimization and deployment tools such as ONNX, TensorRT, TF-Lite, or Go.
Practical experience with real-time streaming video or audio processing pipelines and large-scale synthetic data generation or augmentation.
Experience competing in or using benchmarks from challenges such as DFDC, ASVspoof, NIST SRE/FRVT, or Kaggle.
Familiarity with natural language processing, foundational multimodal architectures, or sequence modeling applied to authentication and spoofing detection.

Responsibilities

Conduct research to develop new capabilities and improve existing technologies for synthetic media detection, multimodal audio and video deepfake identification, and real-time identity authentication.
Design, implement, and evaluate deep learning algorithms for audio-visual deepfake detection, speech synthesis detection, facial manipulation analysis, and biometric authentication.
Design, develop, and maintain internal research packages and pipelines for training, evaluating, and benchmarking machine learning models, ensuring computational efficiency and reproducibility.
Partner with research engineers and engineering teams to transition research prototypes into efficient, scalable, production-ready systems across cloud and edge platforms.
Analyze model behavior, edge cases, attack vectors, and detection gaps, and use findings to make measurable improvements to model robustness.
Participate in code reviews, research reviews, and cross-functional project syncs.
Contribute to patents and peer-reviewed papers for leading computer vision, speech, audio, and machine learning conferences.
Take ownership of assigned research projects and workstreams, communicate progress transparently, and maintain technical integrity and documentation.

Benefits

Competitive compensation package, including RSUs for all employees.
Remote-first work environment with flexibility and autonomy.
Unlimited Paid Time Off.
Health and welfare plans, including one employer-paid employee-only plan.
Employer contribution to a Health Savings Account.
Vision and dental plans for employees and their families.
Paid parental leave for birth, adoptive, and foster parents.
One year of diaper delivery for a new child.
Recurring monthly phone and internet allowance.
Enhanced fertility and GLP-1 benefits.
Annual Learning & Development stipend for professional growth, skill-building, certifications, and continued education.