Lead Engineer, Applied ML

Accountabilities:

• • Own the technical development of an end-to-end AI-driven decision-support platform, working directly with the founder and taking responsibility for architecture, implementation, integration, and delivery.
  • Build ingestion connectors for diverse sensor and data sources using transport protocols such as REST, gRPC, and MQTT.
  • Define unified schemas and metadata models covering identifiers, timestamps, frequency, location, calibration, and other critical sensor attributes.
  • Develop data-fusion pipelines that align streams across time and space while incorporating automated checks for dropouts, outliers, malformed records, and other data-quality issues.
  • Build and train supervised and unsupervised machine-learning models for signal classification and anomaly detection.
  • Optimize ML inference for near-real-time performance through profiling, quantization, pruning, and other appropriate techniques.
  • Produce rigorous model evaluation evidence, including accuracy, precision, recall, and false-positive rates, to support independent validation and review.
  • Develop a live, GPU-accelerated dashboard capable of rendering fused sensor data, overlays, and confidence scores with responsive near-real-time performance.
  • Incorporate usability and human-factors findings into visualization and interface design.
  • Integrate data pipelines, ML models, dashboards, and services into a modular, containerized platform exposed through secure APIs.
  • Support deployment and scenario-based testing within client environments and collaborate with external security specialists to address identified findings.
  • Establish scalable engineering practices, technical standards, and development workflows as the organization and project portfolio grow.
  • Work effectively with specialist subcontractors responsible for independent model review, human-factors assessment, security auditing, and other validation activities.

  Requirements:

  • Strong full-stack engineering background with the ability to design, build, integrate, and troubleshoot complex systems across the technology stack.
  • Demonstrated applied machine-learning experience, including building, training, and deploying models in production or near-production environments rather than solely in research or prototype settings.
  • Experience developing real-time or near-real-time data pipelines, streaming systems, or other latency-sensitive applications.
  • Strong understanding of data ingestion, transformation, integration, schemas, metadata, and data-quality practices.
  • Experience with cloud infrastructure, with AWS preferred, and familiarity with containerized application deployment.
  • Ability to take ownership as the primary or sole technical engineer, operating independently and making sound architectural and implementation decisions with limited engineering support.
  • Strong problem-solving, systems-thinking, and debugging abilities, with the judgment to balance technical rigor, delivery requirements, performance, and reliability.
  • Comfortable collaborating with external specialists and incorporating independent technical, security, and usability feedback into delivered systems.
  • Experience with signal processing, RF data, multi-sensor systems, defence, security, regulatory, or other technically demanding environments is a strong advantage.
  • Experience working in early-stage or highly autonomous environments where requirements evolve quickly and engineers are expected to operate with a high degree of initiative.
  • Dual Canadian and U.S. citizenship is considered a significant advantage.
  • Ability to work primarily remotely from Canada, with EST preferred, and travel periodically based on project requirements.
  • Willingness to work as a full-time employee or, where appropriate, as a 1-year contractor.

  Benefits:

  • Approximate base compensation of $220,000–$450,000 CAD for full-time employment, depending on experience and fit.
  • Equity options for full-time hires.
  • Fully remote position with flexibility around work location.
  • Hybrid collaboration options where appropriate.
  • Periodic project-based travel.
  • Direct collaboration with the founder and significant technical ownership and autonomy.
  • Opportunity to build foundational engineering practices and systems at an early-stage technology company.
  • Exposure to technically challenging AI, multi-sensor analytics, real-time systems, and commercial, regulatory, and government/defence applications.
  • Full-time employment or potential 1-year contractor arrangement.