Company Description Deveillance builds hardware and AI solutions that give individuals and organizations control over when technology can access them. In a world where phones, wearables, AI assistants, and other always-on devices continuously capture behavior, Deveillance is creating an alternative to simply trusting those devices. The company focuses on protecting conversations and actions from unwanted collection, ensuring that personal interactions remain personal. By combining advanced artificial intelligence with purpose-built hardware, Deveillance aims to redefine privacy and agency in modern digital environments. Team members collaborate on mission-driven work that centers user control and data protection.
Role Description This is a full-time, on-site Machine Learning Engineer role based in San Francisco, CA. The Machine Learning Engineer will design, implement, and optimize machine learning models that detect and manage patterns of device interaction while preserving user privacy. Day-to-day responsibilities include researching and prototyping algorithms for pattern recognition, building and training neural network architectures, analyzing model performance using statistical methods, and collaborating with hardware and software teams to deploy models into production systems. The role involves writing clean, efficient code, conducting rigorous experiments, and documenting methodologies and findings for cross-functional stakeholders. The engineer will also contribute to improving system reliability, scalability, and robustness in real-world environments.
Qualifications
- Strong foundation in Computer Science and Algorithms, with experience designing efficient, scalable solutions.
- Hands-on expertise in Neural Networks and Pattern Recognition for real-world data and signal processing tasks.
- Solid understanding of Statistics and probabilistic modeling for model evaluation and performance analysis.
- Proficiency in one or more programming languages commonly used in ML (e.g., Python, C++, or similar) and ML frameworks (e.g., PyTorch, TensorFlow).
- Experience training, deploying, and maintaining machine learning models in production environments.
- Familiarity with privacy-preserving ML techniques, edge or embedded ML, or working with hardware-integrated systems is a plus.
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical field, or equivalent practical experience.
- Ability to work collaboratively in a multidisciplinary team, communicate complex technical concepts clearly, and adapt to evolving requirements.