Senior Software Engineer - Streaming AI (Remote - Ontario / British Columbia)

Confluent5 days ago

Location

Remote, Ontario, Canada

Workplace

Remote

Type

Full Time

Salary

CAD 144,200 – 169,400

Level

Senior

Role

Senior Software Engineer

Posted

Jul 14, 2026

Full TimeRemoteSenior

The role

Summary

As a Senior Software Engineer on Confluent's AI streaming team, you will architect and operate large-scale, high-performance infrastructure enabling AI inference directly on real-time data streams. This role focuses on building distributed systems that eliminate the need for external ML stacks, requiring expertise in cloud infrastructure, systems reliability, and scalable serving layers across AWS, Azure, and GCP. You'll tackle complex challenges in networking, compute optimization, and security while collaborating with core platform teams to deliver production-grade AI capabilities at scale.

What you'll do

Infrastructure Architecture and Design: Design, develop, and operate large-scale, high-performance infrastructure that powers Confluent Cloud's AI streaming capabilities. Take ownership of end-to-end product architecture from user-facing APIs down to production serving layers, ensuring systems are optimized for reliability, scalability, and cost efficiency in handling complex inference workloads.

Distributed Systems Development: Build foundational software addressing distributed systems challenges including consensus algorithms, failover strategies, resource allocation, and state management. Work on systems that enable reliable AI agent execution on streaming data without requiring data movement to external platforms.

Multi-Cloud Platform Optimization: Troubleshoot, improve, and optimize system reliability, observability, and performance across AWS, Azure, and GCP environments. Ensure consistent performance characteristics and cost efficiency across cloud providers while maintaining high availability standards.

Cross-Team Collaboration and Integration: Collaborate with Confluent's broader platform teams whose foundational systems you build upon. Coordinate infrastructure enhancements for real-time data streaming use cases, participate in architectural reviews, and drive integration between AI serving infrastructure and core data plane systems.

Production Operations and Reliability: Own the operational aspects of production AI serving infrastructure, establishing observability standards, defining reliability metrics, and implementing automated solutions for system monitoring. Drive continuous improvements in system resilience and incident response procedures for production environments.

What we look for

Technical

Distributed Systems FundamentalsStrong foundational knowledge of distributed systems principles including consistency models, fault tolerance, consensus algorithms, and distributed tracing. Understanding of networking protocols, load balancing, and inter-service communication patterns.

Statically Typed Programming LanguagesProficiency in Java, Scala, C++, Go, or equivalent statically typed languages used for building production infrastructure. Ability to write clean, maintainable code optimized for performance-critical systems.

Cloud Infrastructure and KubernetesWorking knowledge of cloud infrastructure concepts including containerization, orchestration platforms, networking architectures, and infrastructure-as-code practices for managing complex deployments.

Systems Performance and OptimizationUnderstanding of performance optimization techniques including memory management, CPU efficiency, I/O optimization, and profiling tools. Ability to identify and resolve bottlenecks in high-throughput systems.

Education

Computer Science or Related DegreeBS, MS, or PhD in computer science, electrical engineering, mathematics, or related field preferred. Equivalent professional experience demonstrating mastery of core computer science concepts is acceptable.

Experience

Backend Systems Development2-5 years of industry experience designing, building, and supporting backend systems in production environments. Demonstrated track record of shipping complex distributed systems that handle significant scale and traffic.

Large-Scale Infrastructure OperationsProven experience building and operating large-scale, high-availability systems with expertise in maintaining system reliability, performance monitoring, and incident response in production environments.

Cloud Platform ExperienceHands-on experience with at least one major cloud provider (AWS, Azure, or GCP), including understanding of cloud services architecture, deployment patterns, and cost optimization strategies.

Skills

Required skills

Distributed Systems DesignAbility to architect reliable, scalable systems handling consensus, failover, state management, and data consistency across multiple nodes and geographical regions.

Backend Development in Statically Typed LanguagesProduction-level proficiency in Java, Scala, C++, Go, or similar languages with deep understanding of memory management, concurrency patterns, and performance characteristics.

Cloud Platform ArchitecturePractical expertise with AWS, Azure, or GCP including services like compute instances, databases, networking, load balancing, and monitoring. Understanding of multi-cloud deployment strategies.

Systems Reliability and ObservabilityExperience building observable systems with comprehensive logging, metrics, distributed tracing, and alerting. Expertise in on-call practices, incident response, and postmortem-driven improvements.

High-Performance Systems EngineeringProven ability to optimize systems for throughput, latency, and resource efficiency. Skilled in performance profiling, bottleneck identification, and implementing architectural improvements for scale.

Nice to have

Model Serving InfrastructureExperience building or operating platforms for serving machine learning models at scale, including understanding of inference optimization, batching, and resource management for ML workloads.

LLM and AI Agent InfrastructureExposure to systems supporting large language models or autonomous agents, including challenges around prompt caching, context management, or multi-turn interactions at scale.

Streaming Data SystemsExperience with streaming platforms like Apache Kafka, AWS Kinesis, or similar systems. Understanding of stream processing patterns, state management, and exactly-once semantics in distributed contexts.

Apache Kafka EcosystemFamiliarity with Kafka architecture, including broker operations, topic management, client implementations, or contributing to Kafka-based systems. Understanding of Kafka internals and operational patterns.

Security in Distributed SystemsUnderstanding of security considerations in cloud infrastructure including authentication, authorization, encryption in transit and at rest, and compliance requirements for production environments.

Compensation & benefits

Salary

CAD 144,200 – 169,400 (annual)

Stock options

Available

Benefits

Comprehensive Health Coverage

Medical, dental, and vision insurance plans covering employees and their families with options for various coverage levels and low out-of-pocket costs.

Retirement Planning

Competitive 401(k) matching program (US) or equivalent RRSP matching for Canadian employees, supporting long-term financial security and wealth building.

Flexible Work Arrangements

Remote-first role with flexibility to work from Ontario or British Columbia. Opportunity to maintain work-life balance while accessing Confluent's collaborative culture globally.

Professional Development

Learning stipends, internal training programs, technical certifications, and conference attendance support to advance expertise in distributed systems and cloud technologies.

Stock Options and Equity

Participation in company equity programs providing ownership stake and long-term wealth creation opportunities as Confluent grows.

Paid Time Off

Generous vacation policy, paid sick leave, and company holidays ensuring adequate time for rest, recovery, and personal pursuits throughout the year.

Life and Disability Insurance

Life insurance coverage and short/long-term disability protection providing financial security for you and your family in unexpected circumstances.

Wellness Programs

Mental health resources, fitness stipends, wellness initiatives, and employee assistance programs supporting holistic health and wellbeing.

Parental Leave

Competitive parental leave policies supporting work-life balance for growing families with paid time off and flexible return-to-work options.

Interview process

1
Initial Screening Call — Conversation with recruiter to discuss career goals, experience with distributed systems and cloud infrastructure, and alignment with Confluent's AI streaming mission. This 30-minute call establishes fit and clarifies role expectations.
2
Technical Architecture Discussion — 45-60 minute conversation with senior engineering team member exploring your experience designing distributed systems, handling scale challenges, and operating production infrastructure. Expect questions about past architectural decisions and trade-offs.
3
Coding and Systems Design — Technical assessment involving practical problem-solving around distributed systems design, potentially including a take-home assignment or live coding session focused on infrastructure patterns rather than leetcode-style problems.
4
System Design Deep Dive — In-depth 60-90 minute technical interview with 2-3 engineers discussing large-scale system design, cloud infrastructure challenges, and how you would architect solutions for the specific problems the AI team faces at Confluent.
5
Cross-Functional Collaboration — Conversation with engineers from platform teams to assess collaboration style, communication skills, and ability to work effectively across team boundaries. Discussion of cross-team challenges and integration patterns.
6
Leadership and Values Alignment — Final round with hiring manager or director covering leadership approach, growth mindset, how you navigate ambiguity, and alignment with Confluent's culture of ownership, transparency, and collaborative problem-solving.