About Traceable
Join the future of API security with a company founded by serial innovators reshaping the software industry. Visionaries Jyoti Bansal and Sanjay Nagaraj, creators of AppDynamics (acquired by Cisco for $3.7B), established Traceable with a bold ambition: to become the global leader in API security.
We're experiencing explosive growth, tripling revenue and scaling rapidly to empower enterprises facing evolving API threats. This success is fueled by a winning strategy: unwavering customer obsession, relentless product innovation, and strategic partnerships – all backed by the entrepreneurial expertise behind past industry-defining success. Our cutting-edge solution makes API security manageable for businesses across the globe, ensuring APIs drive growth, not risk.
Join this winning team and make your mark!
About The Role
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.
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
We believe the key to success is bringing together unique perspectives and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
For qualified US: applicants with criminal histories, consideration will be consistent with the requirements of the San Francisco Fair Chance Ordinance. All your information will be kept confidential according to EEO guidelines.
What We Do
Meet the Industry’s Context-Aware API Security Platform
Traceable identifies all of your APIs, and evaluates your API risk posture, stops API attacks that lead to incidents such as data exfiltration, and provides analytics for threat hunting and forensic research. With our solution, you can confidently discover, manage and secure all of your APIs, quickly deploy, and easily scale to meet the ongoing needs of your organization.
- API Risk Posture Management: Tackle API Sprawl, head on. Instantly know where you are exposed. Enjoy automatic and continuous API discovery that gives you comprehensive visibility into all APIs, sensitive data flows, and risk posture – even as your environment changes.
- Stop Data Exfiltration: Detect and block all known and unknown API attacks, including internal and external API attacks, the OWASP web and API top 10, business logic abuse attacks, API abuse, API fraud, and sensitive data exfiltration.
- Threat Hunting: Ensure you are ahead of attackers by searching through the transaction data lake for potential threats. Immediately discover the signs of reconnaissance and take action before the full attack.
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