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Senior Software Engineer, AI
Asana · Vancouver, BC
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What Asana asks for
From Asana's Senior Software Engineer, AI ad, checked 4 Nov 2026.
Technical leadership and delivery
Leading the technical design and delivery of large AI projects, managing milestones and tasks while navigating dependencies across teams.
Distributed systems and AI
At least 6 years building distributed systems or microservices, with practical work in Large Language Models, agentic patterns, or retrieval-augmented generation.
Architectural design documents
Writing comprehensive design documents that evaluate trade-offs like latency and cost to make durable architectural decisions.
Mentorship and code review
Mentoring engineers by providing actionable feedback on code and design, running retrospectives, and setting testing norms.
The ad asks for 6 or more years of software engineering experience. Based in Vancouver, BC with an office-centric hybrid schedule: typically Monday, Tuesday, and Thursday in the office.
How the example answers the ad
Three of the four requirements above, each matched to a line in the example.
Technical leadership and delivery
“Managed project milestones and dependencies across three cross-functional engineering teams”
Handling milestones and cross-team dependencies across three groups provides the specific coordination activities required for technical leadership and delivery.
Distributed systems and AI
“Engineered a distributed microservice layer for financial data processing using Go and gRPC”
Using Go and gRPC for a microservice layer answers the distributed systems part of the requirement, illustrating practical experience in high-concurrency environments.
Architectural design documents
“Authored design documents evaluating latency versus cost trade-offs for GPU resource allocation”
Analyzing trade-offs between latency and cost for GPU resources shows the analytical depth needed for architectural design documents.
The whole example as text
Tyler Bennett · Senior AI Software Engineer
Summary
Senior AI Software Engineer with 6 years of experience in distributed systems and generative AI. Architected retrieval-augmented generation pipelines for high-traffic production environments. Led technical teams through complex model integration projects and cross-functional dependency management. Optimized inference latency by 40 percent through systematic refactoring of microservice communication protocols.
Experience
Senior AI Software Engineer · Meta
Menlo Park, CA · 2025 - Present
- Led technical design and delivery of a multi-modal RAG pipeline serving 50k requests per minute
- Reduced model inference costs by 22 percent by implementing dynamic request batching
- Authored design documents evaluating latency versus cost trade-offs for GPU resource allocation
- Managed project milestones and dependencies across three cross-functional engineering teams
- Mentored four junior engineers through detailed code reviews and bi-weekly architecture design sessions
AI Software Engineer · Stripe
San Francisco, CA · 2023 - 2025
- Engineered a distributed microservice layer for financial data processing using Go and gRPC
- Achieved 99.99 percent system availability by automating failover protocols within the payment pipeline
- Conducted retrospectives that identified and resolved systemic bottlenecks in the deployment lifecycle
- Mentored team members on setting testing norms and establishing rigorous CI/CD standards
- Collaborated with infrastructure teams to reduce infrastructure spend by 15 percent annually
Software Engineer · Shopify
Ottawa, ON · 2022 - 2023
- Developed high-throughput API endpoints to support real-time inventory management for global merchants
- Decreased database query latency by 35 percent through indexing strategy improvements and caching layers
- Orchestrated migration of legacy services to containerized environments using Kubernetes
- Provided code review feedback for a team of six developers to maintain high quality standards
- Analyzed system performance metrics to improve response times for storefront operations
Junior Software Engineer · Uber
Toronto, ON · 2020 - 2022
- Built automated data pipelines processing 2TB of daily logs using Apache Spark and Scala
- Improved test coverage from 60 percent to 90 percent by introducing automated unit testing suites
- Participated in on-call rotations to manage system stability and resolve critical production incidents
- Collaborated with data scientists to deploy machine learning models for dynamic pricing predictions
- Documented API integrations that accelerated the onboarding of external partner services by 20 percent
Education
Master of Engineering in Artificial Intelligence · McGill University
Montreal, QC · 2020 - 2022
- Conducted research on transformer architecture scalability
- Published peer-reviewed paper on low-latency inference patterns
- Organized annual AI symposium for graduate engineering students
Bachelor of Science in Computer Science · University of Toronto
Toronto, ON · 2016 - 2020
- Completed honors thesis on distributed consensus algorithms
- Served as teaching assistant for advanced data structures courses
- Won departmental coding challenge for algorithmic efficiency
Skills
Frequently asked questions
Is the example from an employee?
No. It is an AI-written example for this ad, made to show how a resume can answer it.
Do I need to meet every requirement to apply?
No. The ad states that if you are interested in the role but do not meet every listed requirement, they still encourage you to apply.
What is the interview process like?
The technical interview process is streamlined across the AI organization. After the interview, the company works with you to match you to the most suitable team.
Where do I apply?
On the official Asana posting, through the job link on this page.
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