Machine Learning Engineer (MLE), Artificial Intelligence
Are you applying to the internship?
Job Description
About the Company:
Splunk is a company passionate about making machine data accessible, usable, and valuable to everyone. They are committed to their work, customers, fostering a fun work environment, and prioritizing each other’s success. They are pursuing a disruptive vision and seeking individuals who share their passion for delivering the best customer experience.
Job Description: Machine Learning Engineer (MLE), Artificial Intelligence
This role involves developing core AI/ML capabilities to power Splunk’s product portfolio, enabling customers to achieve digital resiliency. As a Machine Learning Engineer, you will be a key contributor to the Artificial Intelligence group, responsible for:
• Developing the AI/ML platform and infrastructure: This includes building the foundation for key machine learning use cases within the cybersecurity and observability domains of Splunk’s products.
• Integrating generative AI solutions: You will collaborate with software engineers, applied scientists, and product managers to seamlessly incorporate generative AI capabilities into Splunk’s offerings.
• Staying current with AI/ML advancements: Staying abreast of the latest developments in the field and integrating these advancements into Splunk’s technology roadmap is crucial.
• Strategic participation: Active involvement in cross-functional discussions and strategic decision-making concerning AI directions and product roadmaps is expected.
• Mentorship: Guiding and mentoring junior team members.
Responsibilities: The role encompasses development of AI/ML infrastructure, collaboration across multiple teams (software engineers, applied scientists, product managers), integration of generative AI, staying up-to-date on industry trends, and strategic planning.
Requirements:
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience: At least 3+ years of industry experience.
• Technical Skills:
• Experience with containerization and orchestration tools (Docker, Kubernetes).
• Experience with model deployment and serving in production environments.
• Proficiency in version control systems (Git).
• Knowledge of CI/CD principles and tools.
• Familiarity with cloud platforms (AWS, GCP, Azure) and serverless architecture.
• Experience with MLOps platforms (MLflow or Kubeflow).
• Soft Skills:
• Proven ability to work effectively in cross-functional teams, collaborating with data scientists and DevOps teams.
• Strong problem-solving skills and ability to troubleshoot complex issues.
• Excellent communication skills, capable of explaining complex technical information to both technical and non-technical audiences.
Compensation: The base pay range varies significantly based on location (see the provided ranges for different regions). In addition to base pay, the role includes eligibility for incentive compensation and potentially equity or long-term cash awards. A comprehensive benefits package is also offered.
