Intermediate or Senior Software Development Engineer - ML Ops US Federal

About the Role

Key Responsibilities

• Developing frameworks, automation, and tooling to foster a culture of efficiency and innovation.
• Apply technologies like Kubernetes, Docker, and Python to enhance developer scalability in creating innovative ML Runtime Inference applications.
• Implementation and operation of distributed systems and software development including the conception, specifying, designing, programming, documenting, testing, and bug fixing involved in creating and maintaining applications, frameworks, or other software components.
• Developing products and services that empower developers to streamline their interactions with the ML platform.
• Working with public clouds (such as IAAS, AWS, GCP) and applying capacity management principles.
• Deploying and orchestrating containers in production environments, including technologies like Containers, Kubernetes, Service Mesh, ArgoCD and related tools.
• Actively engage with Tech Leads and ML Engineers across teams to elaborate on requirements and drive technical solutions.
• Own and develop features from end to end including infrastructure as code.
• Research, evaluate, prototype and drive adoption of new ML tools with reliability and scale in mind.
• Strong dedication to proactively addressing and resolving issues, automating processes, and empowering engineers to self-service their operational needs for improved productivity.
• Availability for on-call support on a rotational basis.

Requirements

• US Citizenship is required.
• 3 or more years of DevOps experience including Infrastructure automation, building CICD pipelines.
• Good in System design and writing comprehensive technical design docs.
• Proficient in Python programming.
• Design, implement, and maintain robust DevOps pipelines for deploying, monitoring, and scaling machine learning runtime environment.
• Experience using technologies like Kubernetes/Docker to help developers scale their efforts in creating new and innovative products.
• Collaborate with other Machine Learning teams to improve not just the product, but efficiencies in engineering processes.
• Machine learning background.
• Experience with communication protocols, RESTful services, service-oriented architecture, distributed systems, and microservices.
• Building comprehensive monitoring services.
• Prior experience with enterprise SaaS products.
• Experience with monitoring tools like Grafana.
• Passion for creating and maintaining documentation and fixing run books.
• Proficiency in infrastructure automation tools like Terraform, implementing CI/CD pipelines using Git and Jenkins, and applying continuous deployment tools such as ArgoCD.
• BS/MS in Computer Science or a related technical field.
• Excellent problem-solving skills with a focus on creating and maintaining accurate documentation.
• Experience in leading or mentoring other team members and proven team collaboration experience.

Nice to Have

• Experience with monitoring tools like Grafana.
• Experience with enterprise SaaS products.
• Experience with communication protocols, RESTful services, service-oriented architecture, distributed systems, and microservices.
• Building comprehensive monitoring services.
• Proficiency in infrastructure automation tools like Terraform, implementing CI/CD pipelines using Git and Jenkins, and applying continuous deployment tools such as ArgoCD.

Qualifications

• BS/MS in Computer Science or a related technical field.

Benefits & Perks

• The annualized base salary ranges for the primary location and additional locations are listed, with pay ranges from $122,400 to $217,200 USD depending on location and experience.
• Role may be eligible for Workday Bonus Plan or role-specific commission/bonus, as well as annual refresh stock grants.
• Flexible work approach combining in-person and remote work, with at least 50% of time each quarter in the office or in the field.
• Opportunity to create a flexible schedule that caters to personal and team needs.