Build and scale the core systems aircraft technicians and planners use every day, focusing on AI agent systems, architecture, and reliability pipelines.
Compensation
£90k–£120k salary plus meaningful early-stage equity.
Responsibilities
Build and scale the core systems that aircraft MROs rely on every day.
Build AI agent systems end-to-end, from voice interfaces on the hangar floor to workflow orchestration across maintenance operations.
Own projects from architecture to deployment, with autonomy over technical decisions.
Own reliability: infrastructure, observability, and evals for AI in a regulated industry.
Turn aerospace data into trusted pipelines, from maintenance manuals and work orders to airworthiness directives and parts catalogues.
AI Test Engineer responsible for validating AI-enabled solutions in production environments, translating testing strategy into executable frameworks, and ensuring performance thresholds are met.
Responsibilities
Translate AI testing strategy into executable test scenarios across LLM outputs, document classification, extraction accuracy, agent workflows, and edge cases
Design adversarial and boundary test inputs to expose hallucination, misclassification, and failure modes
Validate AI outputs for structure, consistency, accuracy, and production readiness against defined performance thresholds
Build reusable Python-based evaluation frameworks, including output validation, hallucination detection, and scoring mechanisms
Develop parameterized test scripts reusable across features, models, and releases
Requirements
Bachelor’s degree required
Advanced degree in Computer Science, Data Science, or related field preferred
Nice to have: Advanced degree in Computer Science, Data Science, or related field
AI Test Engineering is a hands-on role within VLabs Quality Engineering, responsible for validating the performance, reliability, and integrity of AI-enabled solutions in production environments.
Responsibilities
Translate AI testing strategy into executable test scenarios across LLM outputs, document classification, extraction accuracy, agent workflows, and edge cases
Design adversarial and boundary test inputs to expose hallucination, misclassification, and failure modes
Validate AI outputs for structure, consistency, accuracy, and production readiness against defined performance thresholds
Build reusable Python-based evaluation frameworks, including output validation, hallucination detection, and scoring mechanisms
Develop parameterized test scripts reusable across features, models, and releases
Requirements
Bachelor’s degree
Advanced degree in Computer Science, Data Science, or related field preferred
Nice to have: Background in regulated or compliance-driven environments
Lead and mentor a small team of Applied AI engineers while driving end-to-end delivery of AI programs for strategic customers.
Compensation
The estimated base salary range for this role is $200,000 - $287,500. Additionally, this role is eligible to participate in Snowflake’s bonus and equity plan.
Responsibilities
Lead Customer Programs: Own the full lifecycle of complex, multi-engineer AI engagements
Own AI Quality: Define and measure quality metrics for AI systems
Grow and Mentor Engineers: Provide day-to-day technical leadership and mentorship
Deliver with Velocity: Design, iterate, and ship high-quality ML pipelines and agentic AI solutions
Productionize AI at Scale: Implement lifecycle for AI solutions in secure, large-scale production environments
Build automated test infrastructure and evaluation workflows for AI-driven industrial software
Compensation
Salary is commensurate with experience, and ranges between $ 100,000 CAD - $ 150,000 CAD, excluding bonus and benefits. In addition to base salary, this role includes eligibility for an annual discretionary bonus of 10%
Responsibilities
Build automated test infrastructure for a product developed across multiple sites
Design evaluation methods for agent behavior
Build the assessment workflow into the product
Integrate security checks into the workflow
Keep test expectations independent of implementation
Requirements
Degree in computer science, software engineering, or a related field
Nice to have: agent evaluation frameworks
Nice to have: AI observability tooling (Langfuse, LangSmith, Promptfoo)
Nice to have: safety-relevant or regulated software experience
Nice to have: background in safety-relevant or regulated software
Lead and mentor a small team of Applied AI engineers, owning end-to-end delivery of enterprise AI programs and serving as a senior technical advisor to customers.
Lead the technical transformation of customer interactions, moving from traditional IVR and manual processes towards AI-powered conversational experiences across voice and digital channels.
Responsibilities
Lead the technical transformation of customer interactions
Shape and deliver AI agents and conversational solutions
Define architecture, engineering standards and key technical decisions
Lead a multidisciplinary team of AI and QA Engineers
Collaborate closely with Product, Architecture, Security, Legal, platform teams and business stakeholders across different countries
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