Maven
Role ID:14

Software Engineer - DevOps and MLOps

Location:
San Francisco Bay Area, California USA

Role Description

We are looking to recruit an exceptional Software Engineer - Development and Machine Learning Operations to build and maintain the infrastructure that supports our software development, machine learning models, and AI operations.

In this role you will:

  • Design, implement, and continually improve our CI/CD pipelines to facilitate seamless code integration and deployment.
  • Help define and prioritize infrastructure initiatives that will help with scaling.
  • Automate infrastructure orchestration and configuration management using tools such as Kubernetes, Terraform, Crossplane, Ansible, and similar.
  • Monitor and optimize system performance, availability, and security.
  • Configure and maintain data infrastructure appliances.
  • Troubleshoot and resolve issues related to applications, infrastructure, and deployments.
  • Work closely with our development and AI teams to deliver solutions that increase efficiency and stability, while optimizing for cost.

Qualifications

Must-have:

  • BS or MS in software engineering, computer science, or a related field.
  • Proven experience working with Kubernetes and distributed backend services.
  • Deep experience managing self-hosted CI/CD systems and driving best practices around build and release workflows.
  • Experience with multi-language build systems (e.g., Bazel, Bob).
  • Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
  • Experience with automation tools (e.g., Terraform, Crossplane, Ansible, GitHub Actions, Jenkins) and version control systems (e.g., Git).
  • Strong programming skills in languages such as Python, Go, or Java.
  • Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
  • Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:

  • Experience with MLOps platforms (e.g., Ray, ZenML, MLflow, Kubeflow, Databricks, Vertex AI or SageMaker).
  • Knowledgeable with distributed data systems and streaming platforms such as NATS or Kafka.
  • Experience with monitoring and observability tools (e.g., Prometheus, Grafana, ELK stack).
  • Understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, or Scikit-learn).
  • Experience with edge computing and IoT device management.
  • Knowledge of security best practices and compliance standards in AI/ML environments.
  • Proficiency in database management systems (e.g., PostgreSQL, MongoDB, or Cassandra).
  • Experience with infrastructure-as-code tools (e.g., Terraform, Crossplane, Pulumi).
  • Knowledge of GitOps practices and tools (e.g., Argo CD, Flux).
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