Position Summary:Trulioo is looking for a Senior Software Engineer to lead the architecture and development of high-throughput, low-latency back-end services that power our Document and Biometric Verification platform. Reporting to the Senior Manager, Software Engineering, the Senior Software Engineer will design and operate distributed, event-driven microservices that orchestrate real-time identity verification - ingesting captured documents and biometric data, coordinating ML inference and third-party vendor calls, and returning verification decisions at scale across multiple global regions. You will bridge the gap between developer-facing APIs, asynchronous processing pipelines, and the ML and infrastructure systems that sit behind them.
This role is expected to leverage AI and emerging technologies to improve productivity, enhance decision-making, and continuously optimize how work is performed.
This will be a full-time, permanent position, ideally working out of the Vancouver office on a hybrid model (3-days per week in the office). However, we are open to remote arrangements for the right candidate.
What You'll Be Doing:- Service Architecture: Design, build, and maintain secure, modular, horizontally-scalable microservices
- API & Contract Design: Own the gRPC/Protobuf contracts that drive inter-service communication, and the REST/OpenAPI surfaces exposed to external clients, with versioning and backward-compatibility discipline.
- Event-Driven Pipelines: Build robust, high-throughput streaming pipelines on Apache Kafka - designing topics, consumers, and stream-processing topologies to process transaction lifecycle events and coordinate downstream verification steps.
- Data & State Management: Model and optimize data access across distributed NoSQL databases, search/indexing systems, and caching layers; design for consistency, idempotency, and retention/compliance requirements.
- Workflow Orchestration: Implement durable, multi-step verification workflows using a workflow engine to guarantee reliable execution across service and vendor boundaries.
- Performance & Reliability: Drive sub-second end-to-end verification latency and predictable throughput; tune coroutine concurrency, backpressure, and event-driven autoscaling across a wide range of load profiles.
- Technical Leadership: Drive technical architecture, mentor engineers, and collaborate with Machine Learning, Infrastructure, and Product teams to deploy computer vision models and vendor integrations into production.
- Leveraging AI:
- AI-Augmented Engineering: Use AI coding assistants and internal tooling to accelerate service design, implementation, testing, and code review across the microservices platform.
- Intelligent Pipeline Optimization: Apply AI/ML-driven analysis (anomaly detection, log and metrics insight, capacity forecasting) to continuously tune verification-pipeline performance, throughput, and decision logic.
What You'll Bring:- 8+ years back-end/server software engineering experience delivering production-grade, distributed services or platforms.
- JVM & Kotlin: Deep proficiency with Kotlin (or strong Java transitioning to Kotlin) and modern JVM concurrency, including Kotlin Coroutines and structured concurrency.
- Microservices & APIs: Hands-on experience building service-oriented or microservice architectures with gRPC/Protobuf and/or REST, including API design, versioning, and inter-service communication patterns.
- Distributed Systems & Messaging: Demonstrated experience with event streaming and asynchronous messaging (Apache Kafka strongly preferred), including consumer scaling, exactly-once/idempotent processing, and failure handling.
- Data Stores: Practical experience with distributed NoSQL databases, search/indexing systems, and caching strategies at scale.
- Cloud Native: Comfortable operating services in a public cloud environment on Kubernetes, with familiarity around containerization, service meshes, and infrastructure-as-code.
- Resource Optimization: Demonstrated expertise managing memory, thread/coroutine concurrency, connection pooling, and payload optimization for high-throughput, latency-sensitive workloads.
- AI Expectations:
- AI-Assisted Development: Hands-on daily use of AI coding assistants (e.g., GitHub Copilot, Claude Code, Cursor) to accelerate design, implementation, testing, and code review.
- Critical Evaluation: Ability to critically assess AI-generated code and outputs for correctness, security, and performance before it reaches production.
- Applied AI/ML Literacy: Working understanding of how LLMs and ML models behave in production (latency, cost, failure modes) sufficient to reason about AI-driven components in a backend system.
It's a bonus if you have...- Identity Verification: Experience with IDV (Identity Verification), KYC systems, or integrating with biometric liveness/anti-spoofing and document-authenticity engines.
- Workflow & Orchestration: Familiarity with durable workflow engines (Temporal, Cadence, or equivalent) for coordinating long-running, multi-service processes.
- ML Integration: Experience serving or integrating ML models into back-end services (e.g., Deep Java Library, PyTorch, ONNX, or TensorFlow) and orchestrating third-party vendor APIs.
- Observability & Ops: Experience with modern observability tooling (metrics, logs, and distributed tracing via OpenTelemetry) and running multi-region production services.
- DevOps/CI: Experience with CI/CD pipelines, containerized builds, Helm-based Kubernetes deployments, and secrets management.
Interview Process:At Trulioo, we strive to create an interview experience that is transparent, engaging, and respectful of your time. Our process is designed to help us learn more about your skills and experience while giving you insight into our team, culture, and the impact of the work we do.
Here's what you can expect throughout the interview process:
- Recruiter Interview: 30-minutes
- Hiring Manager Interview: 45-minutes
- Panel Interview: 60-minutes
- Final Leadership Interview: 30-minutes
