Varite
Pay Range: $84.50 - $88.02
Note: Hybrid in San Jose, CA. Minimum 1-2 days onsite.
The Opportunity
Responsibilities
Qualifications:
Profile:
Note: Hybrid in San Jose, CA. Minimum 1-2 days onsite.
The Opportunity
- As a Senior Software Engineer, you will be responsible for developing and maintaining the infrastructure required to deploy, monitor, and manage machine learning models efficiently and effectively.
- This role is focused on building ML-Ops solutions, but general software engineering skills are sufficient.
- The work is critical in bridging the gap between research and engineering, ensuring that our AI solutions are scalable, reliable, and seamlessly integrated into our products.
- This role requires you to thrive in a fast-paced environment, be passionate about AI/ML, and be constantly looking for ways to optimize and automate machine learning workflows.
Responsibilities
- Pipeline Development: Implement, optimize, and maintain CI/CD pipelines for ML systems, including integrations with GitHub workflows and Jenkins.
- Collaboration: Partner with data scientists, frontend engineers, and platform teams to deliver seamless integration of ML models into core evaluation platforms.
- Environment Management: Administer ML development/production environments using cloud-native solutions; optimize for scalability, reliability, and cost.
- Tooling and Automation: Evaluate, build, and deploy automation tools to streamline the end-to-end ML lifecycle.
- Quality & Monitoring: Enhance and develop quality evaluation features and ensure robust monitoring via dashboards and automated alerts.
- Documentation & Best Practices: Champion engineering best practices, promote code quality, and document workflows, tools, and processes for effective team adoption.
Qualifications:
- Python, Typescript, Shell script languages
- Experience with ML pipeline tools (Kubeflow, Airflow, MLflow)
- Services on AWS such as S3, Lambda, DynamoDB
- CI/CD systems (GitHub Actions, Jenkins, GitLab)
- Infrastructure-as-Code experience (Terraform, CloudFormation)
- Containerization (Docker, Kubernetes)
- Communication and documentation skills
- Strong problem-solving skills and the ability to work collaboratively across teams.
- Strong knowledge of ML-Ops a bonus
- CI/CD systems (GitHub Actions, Jenkins, GitLab)
- Infrastructure-as-Code experience (Terraform, CloudFormation)
Profile:
- Master's in computer science or related STEM field
- Minimum 5 years in software engineering; at least 2 years dedicated to DevOps/MLOps in cloud and production environments.
- Industry experiences building end-to-end software pipelines and infrastructure with deep experience with Kubernetes, Infrastructure as Code (Terraform, CloudFormation), AWS, and GCP.
- Expert proficiency in Python; working knowledge of ML frameworks (e.g., PyTorch, TensorFlow, MLflow)
- Practical experience with cloud and NoSQL databases such as DynamoDB; SQL databases a plus.
- Skilled with GitHub Actions, Jenkins, GitLab CI, Docker, and related automation platforms.
- Exposure to Computer Vision, Generative AI (GAN, CLIP, Diffusion, MLLM), and their practical deployment for evaluation systems.
- Experience in integrating ML workflows with user-facing features and backend pipelines.
- Strong problem-solving, excellent written/verbal communication, and the ability to lead and collaborate effectively across teams.