Staff Machine Learning Engineer
New
B
BoulevardSelf-care software
Remote - USAFull-TimeStaff
SalaryFor candidates in NYC, the SF Bay Area, and Seattle, the anticipated base salary range is 136,000 - 171,000 per year. For all other U.S. locations, the anticipated base salary range is 118,400 - 162,800 per year.
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Job Details
- Experience
- 7+ years of combined experience in Data Engineering and Machine Learning roles
- Required Skills
- PythonSQLMachine LearningSnowflakeData engineeringData modelingDatabricksMLOps
Requirements
- 7+ years of combined experience in Data Engineering and Machine Learning roles
- 3+ years of experience building, deploying, and operating machine learning systems in production environments.
- Strong programming skills in Python and SQL, with experience using modern ML frameworks and AI tooling.
- Experience designing scalable data pipelines, data quality, governance and working with modern cloud data platforms such as Snowflake, Databricks, or similar.
- Strong understanding of semantic layer design, MLOps practices, and data modeling principles that support reliable CI/CD for ML workloads.
- Experience building data products that leverage LLMs, embeddings, forecasting, recommendation systems, or other ML technologies.
Responsibilities
- Design, build and deploy production-grade machine learning and AI systems that power customer-facing analytics, automation, and intelligent product experience.
- Develop and operationalize end-to-end ML workflows - from data preparation and feature engineering to model training, deployment, monitoring and continue improvement.
- Build scalable AI solutions leveraging LLMs, embeddings, recommendation systems, forecasting models, and other modern ML techniques.
- Collaborate with Product and Engineering teams to identify high-impact use cases and deliver seamless AI-powered data products.
- Design and evolve semantic data models that enable accurate, consistent, and scalable analytics across the organization.
- Mentor engineers, provide technical leadership, and influence architecture and engineering standards across teams.
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