Lead Engineer, Applied ML (Remote within Canada)

This is a remote position.

Company: Confidential — an early-stage, founder-led technology company (full details shared with shortlisted candidates) Location: Canada (EST preferred) · Hybrid, with periodic project-based travel Type: Full-time — also open to 1-year contractors Reports to: Founder / CEO Compensation: Approx. $220–$450k CAD base + equity options for full-time hires

Our client is an early-stage, founder-led company building AI-driven analytics for multi-sensor data, working across commercial, regulatory, and government/defense programs. Their platform has already been validated in real operational environments with early customers, and they're now extending it into new defense, security, and commercial applications.

They're hiring a Lead Engineer with applied ML depth. You'll work directly with the founder to build the company's technical capability from the ground up. This is a true foundational role with a lot of autonomy.

Your first project extends the platform into a new AI-driven decision-support system for a defence-sector client. It fuses multi-domain sensor data (radio-frequency, infrared, and visual-band), applies ML-based signal classification, and delivers a real-time visualization and decision-support interface. You'll be the primary technical builder across data ingestion, model development, dashboard, and system integration, working alongside a small set of specialist subcontractors (human factors, independent model review, security audit) who validate and stress-test what you build.

Past that first project, you'll help shape the company's engineering practices and pick up new work as the project portfolio grows.

Data pipeline & fusion

• Stand up ingestion connectors for a range of sensor and data sources over common transport protocols (e.g. REST, gRPC, MQTT)
  
• Define a single unified schema and metadata model; identifiers, timestamps, frequency, location, calibration
  
• Align streams across time and space, and add automated quality checks that catch dropouts, outliers, and malformed records
  

Applied ML

• Build and train both supervised and unsupervised models for signal classification and anomaly detection
  
• Tune inference for near-real-time latency. This entails; profiling, quantization, pruning, and similar techniques.
  
• Produce the evaluation evidence (accuracy, precision/recall, false-positive rates) needed to support independent third-party model review
  

Visualization & dashboard

• Build a live, GPU-accelerated dashboard that renders fused data with overlays and per-result confidence scoring.
  
• Design adaptive visual layers that stay responsive at near-real-time refresh under full data load.
  
• Fold in usability findings from an external human-factors reviewer.
  

Systems integration

• Bring the pipeline, models, and dashboard together into one modular, containerized system exposed through secure APIs.
  
• Support deployment and scenario-based testing inside client test environments.
  
• Partner with an external security auditor to close out findings ahead of deployment.
  

• Strong full-stack engineering background. You are comfortable owning a system end to end.
  
• Applied ML experience: building, training, and deploying models in production or near-production settings, not just research or prototyping.
  
• Experience with real-time or near-real-time data pipelines and systems integration.
  
• Cloud infrastructure experience (AWS preferred, reflecting the current stack) and containerized deployment.
  
• Comfortable operating as the sole technical owner, with subcontractor partners handling independent review and validation rather than a peer engineering team.
  
• Bonus: signal processing, RF/sensor data, or defense/regulatory technical environments.
  
• Bonus: Dual citizenship (Canada & USA).