About the role
Company Overview 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. Role Description 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.
Qualifications 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).
