Senior Machine Learning Engineer

About The Role

As a Senior Machine Learning Engineer at Traceable by Harness, 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.

Responsibilities

• 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.

Qualifications

• 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.

Nice-to-Haves

• Experience with API security or cybersecurity applications.
• Knowledge of monitoring tools like Prometheus, Grafana, or custom solutions for model drift detection.
• Familiarity with feature stores and model versioning.

Location: US Remote