Senior Machine Learning Engineer

Harness is a high-growth company that is disrupting the software delivery market. Our mission is to enable the 30 million software developers in the world to deliver code to their users reliably, efficiently, securely and quickly, increasing customers’ pace of innovation while improving the developer experience. We offer solutions for every step of the software delivery lifecycle to build, test, secure, deploy and manage reliability, feature flags and cloud costs. The Harness Software Delivery Platform includes modules for CI, CD, Cloud Cost Management, Feature Flags, Service Reliability Management, Security Testing Orchestration, Chaos Engineering, Software Engineering Insights and continues to expand at an incredibly fast pace.   Harness is led by technologist and entrepreneur Jyoti Bansal, who founded AppDynamics and sold it to Cisco for $3.7B. We’re backed with $425M in venture financing from top-tier VC and strategic firms, including J.P. Morgan, Capital One Ventures, Citi Ventures, ServiceNow, Splunk Ventures, Norwest Venture Partners, Adage Capital Partners, Balyasny Asset Management, Gaingels, Harmonic Growth Partners, Menlo Ventures, IVP, Unusual Ventures, GV (formerly Google Ventures), Alkeon Capital, Battery Ventures, Sorenson Capital, Thomvest Ventures and Silicon Valley Bank.

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

What you will have at Harness

• Competitive salary
• Comprehensive healthcare benefits
• Flexible Spending Account (FSA)
• Employee Assistance Program (EAP)
• Flexible Time Off and Parental Leave
• Quarterly Harness TGIF-Off / 4 days
• Monthly, quarterly, and annual social and team-building events
• Recharge & Reset Program
• Monthly internet reimbursement
• Commuter benefits

The antic