Top Tech Jobs & Startup Jobs in Vancouver

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Design, develop, test, and maintain scalable software applications and systems. Write high-quality code, solve complex technical problems, collaborate with product and engineering teams, participate in code reviews and debugging, contribute to system architecture, and improve engineering processes. The role requires strong software fundamentals, production-scale experience, and familiarity with Databricks, distributed systems, and cloud infrastructure.

Build and maintain software engineering infrastructure for AI development and evaluation workflows. Develop rubrics, review and label preference data, create scalable pipelines, document findings, navigate complex codebases, and improve evaluation quality and consistency. Collaborate with researchers and engineers in an ambiguous, fast-paced environment while applying strong judgment, debugging, communication, and system-design skills.

Build and evaluate MLOps and ML systems tasks for frontier AI training data. Responsibilities include designing technical challenges, writing solutions and evaluation rubrics, profiling and optimizing GPU workloads, debugging distributed systems, improving model performance, and supporting high-throughput LLM inference. The role requires production experience with ML infrastructure, serving systems, GPU accelerators, JAX or PyTorch, and strong technical communication.

Design insurance pricing, reserving, forecasting, and portfolio-risk scenarios for AI model training. Create actuarial analyses and reference answers, evaluate AI outputs for quantitative and methodological accuracy, identify unsupported assumptions and errors, and provide structured feedback. The role requires explaining actuarial methods and business implications to technical and nontechnical audiences while participating in calibration and onboarding sessions.

Design and evaluate life insurance and annuity advisory, underwriting, policy-service, suitability, and replacement scenarios for AI training. Create expert reference responses, assess model outputs for product accuracy, mathematical correctness, suitability, tax treatment, and disclosure compliance, and provide feedback to improve model performance. Participate in onboarding and calibration sessions.

Serve as the internal customer advocate and technical pre-sales lead for finance-heavy billing and revenue recognition solutions. Translate complex billing and revenue requirements into configured demos, sandbox environments, and solution documents. Partner with Sales, Product, and Engineering to support discovery, RFPs, implementations, and handoffs for enterprise customers.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.