Senior Machine Learning Engineer, Traceable

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or a related field
• 5+ years in machine learning engineering or software engineering with significant ML focus, including experience in deploying ML models in production
• Proficiency in Python and familiarity with ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn)
• Experience with CI/CD for ML, containerization (Docker, Kubernetes), and workflow orchestration tools (e.g., Airflow, MLflow)
• Strong knowledge of cloud platforms (AWS or GCP), including managed ML services (SageMaker, Vertex AI)
• Familiarity with distributed computing frameworks (e.g., Spark, Dask) and data pipelines
• Strong problem-solving skills with proven ability to troubleshoot and optimize ML systems in production
• Excellent communication and teamwork skills, with experience working in cross-functional environments
• Ability to thrive in a fast-paced, evolving environment and rapidly adopt new tools and technologies

Desirable

• As a Senior Machine Learning Engineer at Traceable, you will be instrumental in transforming ML models from prototype to production at scale.
• You will work closely with data scientists, MLOps engineers, and product teams to deploy robust, high-performing ML solutions.
• This role requires a blend of engineering, MLOps, and data science skills to streamline model deployment and ensure continuous, reliable operations in the production environments
• Model Productization: Collaborate with data scientists to convert ML models from prototypes to scalable, production-ready solutions. Optimize models for performance, scalability, and resource efficiency
• Integration and Deployment: Develop and maintain enablement pipelines for continuous integration and deployment of ML models, ensuring smooth transitions from development to production
• Scalability and Optimization: Implement distributed systems and leverage cloud-based architectures (e.g., AWS, GCP) to scale ML models and optimize for low latency and high availability
• Model Monitoring and Maintenance: Set up monitoring systems to track model performance in production, detect data drift, and trigger automated retraining when needed
• Innovation and Tooling: Evaluate and integrate new tools, frameworks, and libraries that can improve model deployment speed and robustness
• Documentation and Knowledge Sharing: Document processes, maintain well-structured codebases, promote best practices in ML engineering, and lead internal knowledge-sharing sessions to foster a culture of continuous improvement and technical excellence