Senior Machine Learning Engineer
New
Based in United StatesFull-TimeSenior
Salary161,000 - 273,000 USD per year
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLMachine LearningMLFlowPyTorchTensorflowscikit-learn
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
- 4+ years of experience developing and deploying machine learning systems in production environments.
- Proven experience managing the complete ML lifecycle, including training, evaluation, deployment, monitoring, and iteration.
- Experience designing or working with ML infrastructure, including training pipelines, inference systems, model registries, deployment workflows, and monitoring solutions.
- Strong programming skills in Python and experience with ML frameworks such as PyTorch, scikit-learn, or TensorFlow.
- Experience with cloud-based ML platforms and infrastructure, such as AWS SageMaker, Vertex AI, MLflow, or similar technologies.
- Strong SQL and data modeling skills, with experience handling complex and large-scale datasets.
- Ability to design reliable, maintainable, and observable systems while making effective technical tradeoffs.
- Product-oriented mindset with the ability to define success metrics, validate assumptions, and determine when machine learning is the right solution.
- Strong communication and collaboration skills across engineering, product, analytics, and domain teams.
Responsibilities
- Design, deploy, and maintain production machine learning systems supporting both batch and real-time use cases.
- Build and improve ML lifecycle infrastructure, including training pipelines, inference workflows, deployment processes, monitoring, alerting, and automated retraining systems.
- Partner with engineering, product, data science, and domain experts to translate complex business challenges into effective ML solutions with measurable outcomes.
- Drive the transition of models from prototypes into robust production systems through strong architecture, documentation, testing, and operational practices.
- Develop reusable ML engineering patterns, tools, templates, and documentation to improve developer productivity and engineering quality.
- Create workflows for model evaluation, monitoring, performance optimization, and quality measurement.
- Build systems that support explainability, auditability, and safe integration of ML outputs into products and operational processes.
- Collaborate with data and application engineering teams to establish effective connections between data platforms, ML pipelines, and product applications.
- Make thoughtful technical decisions balancing performance, cost, scalability, complexity, and long-term maintainability.
- Provide technical mentorship and guidance to engineers while promoting strong engineering practices and operational excellence.
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