Software Engineer - DevOps and MLOps

Maven Robotics is building the world’s leading general-purpose AI robots.

We are currently operating in stealth and are growing the world’s best team in AI robotics. We are looking for self-starters that are the world’s best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.

We are looking to recruit an exceptional Software Engineer - Software 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 manage CI/CD pipelines to facilitate seamless code integration and deployment.
• Monitor and optimize system performance, availability, and security.
• Automate infrastructure orchestration and configuration management using tools such as Kubernetes, Ansible, and similar.
• 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.

Must-have:

• BS or MS in software engineering, computer science, or a related field.
• Proven experience standing up a CI/CD system from scratch.
• 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, 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., MLflow, Kubeflow, or SageMaker).
• Knowledge of big data technologies (e.g., Hadoop, Spark, 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., CloudFormation, Pulumi).
• Knowledge of GitOps practices and tools (e.g., ArgoCD, Flux).