We are seeking a talented and experienced Machine Learning Solution Engineer to join our growing team.
As an ML Solution Engineer, you will bridge the gap between our cutting-edge machine learning models and their practical, scalable implementation in customer-facing products.
You will work closely with data scientists, product managers, and software engineers to ensure our ML solutions are robust, performant, and deliver maximum value to our users.
Responsibilities
Design, develop, and deploy end-to-end machine learning pipelines in production environments.
Collaborate with Data Scientists to containerize, optimize, and scale ML models for low-latency inference.
Work with Product Managers to understand business requirements and translate them into technical specifications for ML-powered features.
Evaluate and select appropriate MLOps tools and cloud infrastructure components (e.g., Place) to support ML lifecycle management.
Develop robust monitoring, logging, and alerting systems for deployed models to track performance and detect drift.
Conduct technical demonstrations and serve as a subject matter expert for ML capabilities to internal teams and external stakeholders, including during the kickoff event on Date, which will be accessible via this link: Calendar event.
Write and maintain technical documentation, including API specifications, deployment guides, and best practices, which will be stored in this shared folder: File.
Troubleshoot and resolve production issues related to ML services.
Minimum Qualifications
Bachelor's degree in Computer Science, Engineering, or a related technical field.
5+ years of experience in Computer Vision model development or a similar role.
Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Solid understanding of cloud platforms (e.g., AWS, Azure, GCP).
Preferred Qualifications
Master's degree or PhD in a quantitative field.
Experience in designing and implementing CI/CD pipelines for ML models.
Communication skills, with the ability to explain complex technical concepts to non-technical audiences.
Prior experience working in an Agile development environment.
