Staff Engineer, Data and AI

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Engineer, Data and AI based in Canada .

This is a senior technical leadership role focused on building and scaling the data and AI infrastructure that powers high-impact products, experimentation, personalization, and trusted decision-making. You will help translate strategic business and product opportunities into scalable data and AI solutions, from early exploration through production. The role combines hands-on engineering with architecture, technical leadership, and cross-functional collaboration. You’ll design reliable batch and real-time data systems while improving platform scalability, observability, security, and cost efficiency. You’ll also help advance modern AI capabilities, including LLM infrastructure, vector retrieval, evaluation, and agentic workflows. As a Staff-level engineer, you’ll raise engineering standards through mentorship, design leadership, and influence across teams.

• Partner with product managers and cross-functional teams to translate business and product opportunities into scalable data and AI solutions, from initial exploration through production deployment.
• Design, build, and operate reliable batch and real-time data pipelines supporting reporting, experimentation, machine learning, feature generation, model evaluation, personalization, and production inference.
• Evolve cloud-native data and ML platform architecture across orchestration, storage, compute, streaming, and data access, using technologies such as Airflow, Kafka, Redshift, Glue, vector databases, and related services.
• Establish and improve data quality and trust through robust data modeling, schema evolution, data contracts, automated testing, lineage, privacy controls, freshness monitoring, and recoverability practices.
• Develop reusable tools, engineering standards, and self-service workflows that enable teams to discover data and build dependable data and AI solutions with greater independence.
• Maintain platform resilience and operational excellence through observability, alerting, runbooks, incident response, on-call participation, performance optimization, reliability improvements, and cost management.
• Lead architectural decisions and technical initiatives across data and AI infrastructure while reducing technical debt and establishing scalable engineering patterns.
• Contribute to the development and adoption of modern AI and agentic workflows, including embeddings, vector retrieval, LLM evaluation and observability, and production AI systems.
• Mentor engineers through design reviews, code reviews, technical guidance, and hands-on coaching while helping raise the overall technical bar.
• Participate in an on-call rotation and take ownership of the reliability and operational health of critical data and AI systems.

Requirements:

• 7+ years of software engineering experience, with substantial experience building distributed systems, data platforms, ML platforms, or comparable production infrastructure.
• Proven hands-on experience designing, building, and operating large-scale batch and/or streaming data systems, ideally with technologies such as Kafka, Spark, and workflow orchestration platforms.
• Strong software engineering skills with the ability to build maintainable, testable, production-grade systems using Python and/or comparable programming languages.
• Advanced data modeling and SQL expertise, with experience creating scalable, reliable data models and data products.
• Experience designing and operating cloud-native infrastructure using AWS, GCP, or comparable cloud platforms, including infrastructure as code, containers, orchestration, and managed data services.
• Demonstrated experience taking data, ML, or AI systems into production, including deployment, reliability, observability, evaluation, and ongoing operational ownership.
• Strong understanding of distributed data architecture and the ability to make sound trade-offs across compute, storage, orchestration, streaming, infrastructure, scalability, and cost.
• Practical experience with modern AI infrastructure, such as embeddings and vector retrieval, LLM evaluation and observability, or agentic workflows.
• Strong operational mindset with experience in monitoring, incident response, on-call practices, runbooks, performance tuning, and building resilient systems.
• Demonstrated technical leadership, including architectural decision-making, mentoring, code and design reviews, and the ability to influence engineering direction across teams.
• Strong collaboration and communication skills, with the ability to work effectively with technical and non-technical stakeholders.
• Professional-level English proficiency; resumes and application responses should be submitted in English.
• Ability to work full-time from an eligible location in the United States, Canada, or Mexico. For U.S. employment, eligible locations include AK, AZ, CA, CO, CT, DC, FL, GA, IL, IA, KS, MD, MA, MI, MN, MO, NJ, NY, NC, OR, PA, RI, TX, VT, VA, and WA.

Benefits:

• Remote-first flexibility: Full-time opportunity with eligible employees based in the United States, Canada, or Mexico, subject to approved locations.
• U.S. annual base salary: $236,000 in San Francisco and New York; $224,000 in Austin, Boston, Chicago, Washington, DC, Los Angeles, and Seattle; and $200,500 in other eligible U.S. locations.
• Canadian annual base salary: $221,500 CAD in Vancouver and Toronto; $205,500 CAD in Victoria and Calgary; and $202,000 CAD in other eligible Canadian locations.
• Mexico annual base salary: $1,727,000 MXN across eligible locations in Mexico.
• Pay equity: Compensation is determined using predetermined salary scales based on role level and local cost of labor.
• Location-based benefits and perks: Benefits packages vary depending on the employee’s location.
• Professional growth: Opportunities to mentor engineers, influence technical strategy, and contribute to advanced data and AI initiatives.
• Inclusive workplace: A collaborative environment that values diverse experiences, skills, and perspectives.
• Accessibility support: Reasonable accommodations are available throughout the recruitment process for candidates who need them.
• Structured hiring process: Recruiter screen, hiring manager screen, technical assessment, team interview, and executive interview.