Machine Learning Engineer

Role overview Join an AI development team building machine learning models that power real products and services. The role spans research through production deployment, with opportunities to work across data, modeling, and infrastructure layers. It is based in Berkeley, CA with a remote option and is full-time.

Responsibilities - Design and implement machine learning models for a range of applications - Build data processing pipelines and feature engineering systems - Optimize models for performance, accuracy, and computational efficiency - Partner with data scientists and software engineers to integrate models into production - Monitor deployed models, address issues, and ship improvements over time - Research and apply new ML techniques, then document architectures and experimental results

Requirements - M.S. or Ph.D. in Computer Science, Machine Learning, or a closely related field - 3+ years of experience building and deploying machine learning models - Strong Python programming with hands-on use of PyTorch or TensorFlow - Practical experience with data processing, feature engineering, and model evaluation - Solid grasp of supervised and unsupervised learning fundamentals - Familiarity with software engineering practices such as version control, testing, and CI/CD, plus cloud platforms and ML infrastructure

Nice to have - Deep learning and neural network experience - MLOps tools and practices - Distributed computing and large-scale data processing - Background in NLP, computer vision, or reinforcement learning - Track record deploying models live and contributing to open-source ML projects or research publications