Job Description
Machine Learning Engineer
What you'll do:
- Research, develop, and implement the state-of-the-art solutions in AI and ML to develop and deploy scalable solutions into production.
- Collaborating and coordinating with people in a range of roles, including product managers, designers, engineers, and other key product stakeholders to define requirements, scope, and architecture for new features and enhancements.
- Pushing forward what we're doing with AI technology -- not just executing but helping to discover and keep Procore on the leading edge.
- Being an excellent communicator and sharing knowledge by clearly articulating results and ideas to customers, managers, and key decision makers.
- Foster a healthy and inclusive team environment, provide technical guidance to other engineers, and act as a mentor.
What we're looking for:
- Hands-on experience with model development using PyTorch and TensorFlow, including training and evaluating a variety of architectures such as object detectors, image classifiers, and transformer-based models.
- Experienced in prompt engineering and the development of LLM-based solutions, with the ability to design and integrate large language models into machine learning pipelines for practical applications.
- Well-versed in the end-to-end design of ML workflows, including data collection, annotation strategies, feature engineering, model training, hyperparameter tuning, and performance evaluation.
- Over 3 years of Python development experience, with a strong focus on writing scalable, maintainable, and production-ready code following industry best practices for ML pipeline development.
- Skilled in containerization and deployment using Docker, with experience building microservices for ML model inference and integrating them into larger system architectures.
- Proficient in version control using Git, with familiarity in maintaining CI/CD pipelines
- Strong understanding of Agile software development methodologies, with a proven ability to deliver high-quality, iterative results in fast-paced, collaborative team environments.
Preferred Qualifications:
- Experience deploying machine learning services using Helm and Kubernetes, including writing and managing Helm charts for scalable, maintainable, and version-controlled deployments in cloud-native environments.
- Familiarity with distributed computing frameworks such as Ray (or similar orchestration tools), with the ability to design, schedule, and manage parallel ML workloads and pipelines across multiple nodes or containers.
Additional Information
Perks \& Benefits
About Us
