Senior Software Engineer, Backend, AI Platform

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design and develop scalable backend services, APIs, data pipelines, and platform infrastructure throughout the Sureel stack.

• Take ownership of systems from architecture and implementation to deployment, observability, debugging, and iteration.

• Create infrastructure for high-volume AI, media, attribution, search, retrieval, and enterprise workloads.

• Transition experimental models and algorithms into dependable production services alongside the AI team.

• Enhance MLOps capabilities related to model deployment, inference, versioning, evaluation, monitoring, and reproducibility.

• Manage and optimize DevOps and cloud infrastructure, which includes CI/CD, infrastructure-as-code, containers, orchestration, security, observability, and deployment reliability.

• Develop enterprise functionalities such as authentication, RBAC, multi-tenancy, auditability, bulk operations, integrations, and configurable workflows.

• Make architectural decisions concerning databases, queues, caching, distributed systems, compute, storage, networking, reliability, and cost.

• Collaborate with Product, customers, and AI partners to transform complex requirements into scalable technical solutions.

• Create internal tools and automation to support engineering and research teams.

• Utilize coding agents, agentic frameworks, AI-assisted debugging and research, automated testing, and emerging development workflows.

⛳️ Requirements

• Senior software engineer with substantial expertise in backend and platform engineering.

• Hands-on experience designing, building, deploying, and operating significant production systems.

• Highly skilled in a modern backend programming language.

• Proficient in working extensively with TypeScript/Node.js.

• Strong understanding of databases, networking, concurrency, APIs, distributed systems, queues, caching, cloud infrastructure, and failure modes.

• Significant experience with cloud, DevOps, and production infrastructure.

• Knowledge of model serving, GPU workloads, inference, data pipelines, embeddings, evaluation, and MLOps.

• Experience incorporating AI into the engineering workflow.

• Ability to think critically from first principles and tackle challenging technical problems.

• Comfortable operating in a fast-paced startup environment with considerable ownership and ambiguity.

• Preferred additional experience with GCP, Kubernetes, Docker, Terraform, AWS DynamoDB or another NoSQL database, Meilisearch, vector search, distributed processing, GPU infrastructure, enterprise SaaS, developer APIs or SDKs, TypeScript/React, agentic development frameworks, or large-scale audio and media systems.

🏝️ Benefits

• Performance-based annual bonus.