Principal Engineer, Federated Learning

Details

The quality of AI models is only as good as the data they were trained on, and large datasets are necessary to extract complex and predictive patterns. Compared to classical centralized training, federated learning is a privacy-preserving, distributed learning paradigm which tackles the challenges associated with learning from data in a decentralized way.

We believe federated learning will drive transformative changes across industries like healthcare, financial services, scientific computing, and government. Imagine developing AI solutions that enable hospitals to collaborate on life-saving research without compromising patient privacy, or financial institutions to enhance fraud detection while safeguarding sensitive data. Envision edge AI applications that revolutionize autonomous driving, creating vehicles that learn and adapt in real-time.

Come join NVIDIA FLARE team (https://developer.nvidia.com/flare) to build our open source federated learning solutions. Your work will drive the next wave of innovation, empowering organizations to unlock the full potential of their data while maintaining the highest standards of security and privacy.

Responsibilities

• Lead the design and development of cutting-edge federated learning solutions.
• Collaborate closely with researchers worldwide to advance federated learning algorithms and techniques.
• Work with cross-functional teams across NVIDIA, including data scientists, software engineers, and industry experts, to integrate federated learning solutions into real-world applications.
• Optimize federated learning systems for performance, scalability, ease of use, reliability, and security.
• Leverage NVIDIA's hardware and software platforms to enhance federated learning solutions.
• Enhance federated learning solutions with the highest standards of data privacy, security, regulatory compliance, and best practices.
• Mentor and guide junior engineers; establish best engineering practices and processes; foster a culture of continuous learning and innovation.

Requirements

• MS or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent experience).
• 12+ years of work or equivalent experience delivering high-performance software systems.
• 8+ years of architect experience designing and developing distributed systems.
• 5+ years hands-on experience with distributed machine learning technologies such as Distributed PyTorch, Horovod, Ray, and MPI.
• 5+ years working experience with machine learning libraries such as Llama, NeMo, PyTorch, TensorFlow, XGBoost, and scikit-learn.
• Outstanding skills in system and API design.
• Excellent hands-on programming skills in Python and C++.
• Advanced knowledge and experience with communication and network protocols such as gRPC, HTTPS, and TLS.
• Experience with enterprise security such as PKI, authentication, and authorization.
• Excellent analytical and problem-solving abilities and a creative approach to tackling complex technical challenges.
• Strong technical leadership with a strategic mindset, able to envision and drive long-term goals beyond immediate tactical tasks.

Ways to Stand Out (Preferred)

• Working experience in federated learning frameworks such as FLARE, Flower, OpenFL, PySyft, and TensorFlow Federated.
• Experience designing generative AI and agentic AI solutions.
• Hands-on experience developing edge-based AI solutions.
• Architect experience in major open-source projects.
• Direct working experience with NVIDIA software, hardware, and SDKs.

Compensation & Benefits

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 248,000 USD - 391,000 USD. You will also be eligible for equity and benefits: https://www.nvidia.com/en-us/benefits/.

Applications for this job will be accepted at least until September 19, 2025.

NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We do not discriminate (including in hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.