Senior Machine Learning Engineer
New
United StatesFull-TimeSenior
Salary153,000 - 198,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLPyTorchAirflowTensorflowdbtscikit-learn
Requirements
- 5+ years of professional experience in machine learning engineering, software engineering, or data engineering roles.
- Strong proficiency in Python and SQL with hands-on experience building production systems.
- Proven track record of designing, building, and operating large-scale data and ML pipelines.
- Experience deploying and maintaining machine learning models in production environments.
- Solid understanding of the full ML lifecycle, including feature generation, training, deployment, and monitoring.
- Experience with cloud environments, particularly AWS.
- Familiarity with orchestration and data tools such as Airflow, dbt, or similar frameworks.
- Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Strong software engineering practices including testing, debugging, documentation, and system design.
- Experience with feature pipelines or feature store architectures supporting training and online inference.
- Exposure to ranking, recommendation, or decisioning systems is a strong plus.
- Ability to work effectively in ambiguous environments and translate product needs into ML solutions.
Responsibilities
- Own the full machine learning lifecycle, including feature engineering, data pipelines, model training, deployment, monitoring, and retraining in production environments.
- Design and build scalable, reliable data and feature pipelines, including feature store patterns ensuring consistency across training and inference workflows.
- Develop and optimize ML models for ranking, recommendation, classification, regression, and decisioning use cases.
- Implement and maintain batch scoring pipelines and real-time inference services with strong standards for latency, reliability, and performance.
- Collaborate with data scientists to operationalize models and build experimentation frameworks for evaluation and iteration.
- Partner with software engineers to integrate ML models into production systems, APIs, and customer-facing applications.
- Establish observability and monitoring for ML systems, including data drift, feature quality, model performance, and system health.
- Support rapid experimentation and safe deployment strategies for new models and iterations.
- Contribute to architecture design, technical documentation, and best practices for ML engineering across teams.
- Mentor peers through code reviews, technical discussions, and design guidance while contributing to platform-wide ML decisioning systems.
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