Description
As a Research Scientist II on our core Research team, you will independently drive research initiatives, deliver reproducible experimental results, and help translate machine learning models into real-world product solutions across deepfake video, deepfake audio, and voice/identity authentication.
Responsibilities:
- Conduct research to develop new functionalities and improve existing technologies for synthetic media detection, multimodal deepfake identification (audio and video), and real-time identity authentication.
- Design, implement, and evaluate deep learning algorithms related to audio-visual deepfake detection, speech synthesis detection, facial manipulation analysis, and biometric authentication.
- Design, develop, and maintain internal research packages and pipelines to train, evaluate, and benchmark ML models, ensuring computational efficiency and absolute reproducibility.
- Partner closely with research engineers and engineering teams to transition complex research prototypes into efficient, scalable, production-ready systems across cloud and edge platforms.
- Support targeted investigations by analyzing model behavior, edge cases, attack vectors, and detection gaps, turning insights into measurable improvements in model robustness.
- Participate in regular code and research reviews, cross-functional project syncs, and contribute to patents and peer-reviewed papers for top-tier computer vision, speech, audio, and machine learning conferences.
Requirements:
- Master’s degree or PhD in Computer Science, Artificial Intelligence, Electrical Engineering, Mathematics, or a related quantitative field (or equivalent industry research experience).
- 3+ years of professional or post-graduate research experience in Deep Learning, with a dedicated focus on at least one core area: computer vision (facial analysis, video processing), speech/audio processing (speaker recognition, speech synthesis detection), or voice biometrics and identity authentication.
- Hands-on research experience in audio-visual deepfake detection, synthetic media identification, or multimodal biometric verification.
- Strong programming proficiency in Python with a proven ability to build, maintain, and structure reproducible research tooling, experimentation frameworks, or evaluation workflows.
- Hands-on mastery of modern machine learning frameworks such as PyTorch, TensorFlow, or Keras.
- A proven track record of successful, timely project delivery and the ability to translate research findings into practical improvements, supported by peer-reviewed publications or patent contributions.
Nice-to-haves:
- Functional programming experience in C/C++ or familiarity with model optimization and deployment tools (e.g., ONNX, TensorRT, TF-Lite, Go).
- Practical experience with real-time streaming video or audio processing pipelines and large-scale synthetic data generation/augmentation.
- Experience competing in or leveraging benchmarks from prominent challenges (e.g., DFDC, ASVspoof, NIST SRE/FRVT, Kaggle).
- Familiarity with natural language processing (NLP), foundational multimodal architectures, or sequence modeling applied to authentication and spoofing detection.
Benefits:
- Competitive compensation package, including RSUs (Restricted Stock Units) for all employees.
- Remote-first environment.
- Unlimited Paid Time Off (PTO).
- Generous health and welfare plans.
- Best-in-class Health Savings Account (HSA) employer contribution.
- Low-cost vision and dental plans.
- Paid Parental Leave.
- One year of diaper delivery.
- Recurring monthly phone and internet allowance.
- Enhanced fertility and GLP-1 benefits.
- Annual Learning & Development stipend.