ML Engineer-Databricks
San Jose, CA, United StatesYesterday
Free account needed to apply or save. It takes a minute, and we'll bring you right back to this job.
Skills
About the role
- Design and develop scalable ML models and workloads on cloud ML platforms such as Azure ML Studio, AWS, or GCP.
- Facilitate and participate in the preparation of ML models and their scoring processes
- Develop and support the ML portion of the DT GoToMarket strategy to maximize the distribution of ML predictions and recommendations, including publish microservices/APIs
- Incorporate workflows into ML Model Engineering processes, to automate, monitor, and produce alerts
- Implement performance tuning and model optimization
- Stay current on emerging ML platforms and technologies and recommend solutions that would enhance current systems or implementationCollaborate with Product and Engineering team members to follow all documented architecture, design & deployment processes to ensure compliance with policies
Skills & background:
- Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or equivalent field, with strong communication and cross-functional collaboration skills.
- Bachelor's degree in a technical field such as computer science, computer engineering or related field required
- 5+ years of experience as AI/ML Engineer
- Ability to work as part of a team and collaborate, as well as work independently or with minimal direction
- Excellent written, presentation, and verbal communication skills
- Experience in Machine Learning, Data Engineering, ML Infrastructure, or related fields, with strong foundations in probability, statistics, and machine learning.
- Hands-on experience with Databricks, including Unity Catalog, and cloud ML platforms such as Azure ML Studio, AWS, or GCP.
- Experience building and scaling end-to-end ML systems, ETL/ELT pipelines, and data workflows using Python, NumPy, Pandas, and SQL.
- Experience building scalable APIs and microservices using FastAPI, with Snowflake or equivalent data warehouse technologies.
- Experience deploying production workloads using Docker and Kubernetes, with a strong understanding of scalable, reliable software engineering practices.
Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries of the position across all US locations. Within the range, individual pay is determined by work location and additional job-related factors, including knowledge, skills, experience, tenure and relevant education or training. The pay scale is subject to change depending on business needs. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Additional compensation may include benefits, discretionary bonuses, and equity