ML Engineer-Databricks
San Jose, CA
- Pay
- Pay not listedSimilar roles state $165K–$239K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and develop scalable ML models and workloads on cloud ML platforms
Publish ML predictions and recommendations as microservices or APIs
Build workflows to automate and monitor ML model engineering, with alerts
Implement performance tuning and model optimization
Prepare ML models and their scoring processes
From the employer’s posting
Design and develop scalable ML models and workloads on cloud ML platforms
including publish microservices/APIs
Incorporate workflows into ML Model Engineering processes, to automate, monitor, and produce alerts
Implement performance tuning and model optimization
Facilitate and participate in the preparation of ML models and their scoring processes
Education & alternatives
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
- 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
Tools in this posting
- Python
- SQL
- AWS
- Azure
- Databricks
- Docker
- GCP
- Kubernetes
- Snowflake
- FastAPI
- NumPy
- pandas
Source — Tool mentions in context
- 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.
ML Engineer-Databricks1- 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
- 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
- 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.
Posting record
Report an errorActively hiringFirst seen by us 4 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 29, 2026 · 4 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 1, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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- Ask the employer about the pay range before committing time to the process.
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Job description
- 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
Source details
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- Location & working pattern
San Jose, CA
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