Staff Software Engineer, IDSM

Accountabilities

• Scalable Platform Engineering: Design, develop, and maintain high-performance backend services that support enterprise-scale data science and machine learning workflows.

• Cloud Model Deployment: Collaborate with customers and internal teams to design and implement model deployment solutions integrating with platforms such as AWS SageMaker and Azure Machine Learning.

• Model Lifecycle Management: Enhance capabilities across model development, training, registration, deployment, and ongoing management to improve reliability and efficiency.

• Data Science Resource Discovery: Help design and launch a centralized data science catalog that enables users to discover, explore, and summarize resources across the platform.

• Model Monitoring and Observability: Integrate monitoring capabilities that provide a comprehensive view of deployed model health, performance, and operational status.

• Metadata and Discoverability: Expand tagging and metadata functionality across platform entities to improve organization, tracking, reuse, and resource discovery.

• LLM Hosting and Infrastructure: Extend large language model hosting capabilities to meet customer requirements for scalability, performance, and operational logging.

• API Design and Integration: Build secure, reliable, and scalable APIs, including RESTful APIs and gRPC services, and integrate backend systems with frontend interfaces and third-party services.

• Performance Optimization: Profile, troubleshoot, and optimize backend applications and distributed workloads across cloud environments and containerized infrastructure.

• Testing and Delivery Automation: Implement comprehensive unit, integration, and end-to-end testing practices, and contribute to robust continuous integration and continuous delivery (CI/CD) pipelines.

• Cross-Functional Collaboration: Partner with engineering, product, and customer-facing teams to translate technical and business requirements into practical, maintainable solutions.

• Technical Leadership: Contribute to architectural decisions, share engineering best practices, support team learning, and continuously improve platform quality and performance.

Requirements:

• At least 8 years of experience in software engineering, primarily in an individual contributor capacity.

• Strong knowledge of artificial intelligence and machine learning systems, including model development, training, optimization, deployment, and lifecycle management.

• Demonstrated experience designing, building, and maintaining scalable backend systems in distributed computing environments.

• Proficiency in designing and implementing secure, high-performance APIs, including RESTful APIs and/or gRPC.

• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

• Strong understanding of backend performance profiling, troubleshooting, and optimization in cloud-based or distributed environments.

• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes.

• Experience implementing automated testing strategies, including unit, integration, and end-to-end tests, as well as maintaining CI/CD pipelines.

• Ability to collaborate effectively across engineering teams and integrate backend services with frontend applications and external systems.

• Strong analytical and problem-solving skills, with the ability to address complex technical challenges and deliver maintainable, reliable solutions.

• Excellent communication skills and the ability to work collaboratively with technical stakeholders and customers.

• A growth mindset, curiosity, and a willingness to learn, share knowledge, and continuously improve engineering practices.

• Experience with distributed computing frameworks such as Apache Spark, or machine learning platforms such as AWS SageMaker and Azure Machine Learning, is an advantage.

• Familiarity with model registries, model monitoring, enterprise MLOps, and LLM hosting infrastructure is beneficial.

• Ability to work remotely from Canada.

Benefits:

• Competitive compensation: Annual US base salary range of USD $200,000–$235,000. The final range may vary based on experience, qualifications, and location.

• Equity opportunities: Potential eligibility for equity compensation.

• Performance incentives: Additional company bonus opportunities may be available.

• Retirement benefits: Access to a 401(k) plan, subject to applicable eligibility and location requirements.

• Health coverage: Medical, dental, and vision benefits, subject to the applicable benefits package.

• Wellness support: Potential wellness stipends to support employee well-being.

• Remote work flexibility: Opportunity to work remotely from Canada.

• Technical ownership: Meaningful responsibility for designing and improving enterprise AI and machine learning platform capabilities.

• Complex engineering challenges: Work on distributed systems, cloud infrastructure, model monitoring, and LLM hosting at enterprise scale.

• Collaborative culture: Join a team that values continuous learning, open communication, diverse perspectives, and ongoing improvement.

• Professional development: Opportunities to deepen expertise in AI/ML infrastructure, cloud engineering, and modern software architecture.