Staff Machine Learning Engineer
New
North AmericaFull-TimeStaff
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonGCPMachine LearningPyTorchAzureTensorflowA/B testingscikit-learn
Requirements
- Bachelor’s degree or higher in Computer Science, Engineering, Data Science, or a related technical field.
- Proven experience implementing machine learning algorithms and solutions at scale in production environments.
- Demonstrated experimental mindset, adept at hypothesis testing, experimentation design, and rigorous validation techniques.
- Expert proficiency in Python and experience with frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and related ML technologies.
- Extensive experience building and deploying scalable ML pipelines and working within cloud infrastructure environments such as AWS, GCP, or Azure.
- Strong understanding of data structures, algorithm design, and performance optimization techniques.
- Exceptional problem-solving abilities, coupled with excellent collaboration and communication skills.
- Track record of technical leadership, mentorship, and successful cross-functional collaboration in agile, dynamic environments.
Responsibilities
- Design, build, and maintain robust and scalable machine learning models and pipelines tailored to enhance our marketplace platform and GenAI managed solutions.
- Lead experimentation processes, rigorously validating models through A/B testing, statistical analyses, and iterative improvements.
- Collaborate closely with data scientists, software engineers, product managers, and client teams to define requirements, build solutions, and deliver impactful, high-quality products.
- Continuously improve existing ML solutions, ensuring they are performant, efficient, and aligned with evolving business objectives.
- Ensure adherence to best practices in code quality, data integrity, and infrastructure reliability, with a strong emphasis on scalability and maintainability.
- Mentor junior engineers and contribute actively to team growth, knowledge sharing, and fostering an experimental, innovation-driven engineering culture.
- Stay current with advancements in machine learning, AI methodologies, and marketplace technologies to propose innovative solutions proactively.
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