Lead Engineer, Applied ML

About the opportunity

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Engineer, Applied ML based in Canada. This is a foundational engineering role within an early-stage, founder-led technology environment focused on AI-driven analytics for multi-sensor data. You will work directly with the founder to build technical capabilities from the ground up and take ownership of complex systems end to end. Your initial focus will be a real-time AI decision-support system combining radio-frequency, infrared, and visual-band sensor data. You will span data ingestion, data fusion, applied machine learning, visualization, APIs, and system integration. The role offers significant autonomy, with specialist external partners supporting areas such as human factors, model validation, and security. You will work on technology intended for demanding commercial, regulatory, and government/defence applications. As the project portfolio expands, you will also help establish engineering practices and shape the technical direction of future products 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. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Qualifications

• : 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
• , performance, and reliability.