# Applied ML Engineer - Model R&D at Jobgether > Remote · Canada · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/lever%3Ajobgether%3A85c00f38-52e2-4375-aa50-68c5ddd4e1aa (read 2026-11-13). ## Facts (observed) - Company: [Jobgether](https://alderhq.app/companies/jobgether) - Location text: Canada - Where it can be done from (inferred): Remote · Canada (evidence: "Location: Canada") - Posted: 2026-11-12 (1 days ago) - First seen by alder: 2026-11-12 · last verified: 2026-11-13 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.lever.co/jobgether/85c00f38-52e2-4375-aa50-68c5ddd4e1aa - Skills listed (inferred): Rust ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Canada, n 44): p25 CAD 125k, median CAD 154k, p75 CAD 177k ## Description (the employer's text) ### Accountabilities: - Transform research-grade machine learning code and experimental prototypes into production-ready, maintainable, and scalable software systems. - Take ownership of end-to-end ML deployment pipelines, including data ingestion, feature engineering, model integration, inference, serving, monitoring, and reliability. - Work closely with machine learning researchers and engineering teams to translate research objectives and technical concepts into robust, observable production solutions. - Introduce and strengthen structured software development practices, including task management, Git workflows, code reviews, testing, and continuous integration and deployment (CI/CD). - Identify architectural weaknesses, technical debt, and engineering quality issues, recommending and implementing solutions that improve maintainability, scalability, and system performance. - Build and maintain monitoring, logging, alerting, and observability systems to detect issues proactively and ensure reliable operations. - Contribute to the transition toward Rust-native infrastructure by supporting the adoption, integration, and development of Rust-based systems. - Participate in applied ML research and experimentation, helping evaluate promising approaches and translate relevant findings into practical engineering solutions. - Promote a culture of technical excellence, shared ownership, continuous improvement, and constructive collaboration across the team. ### Requirements - Professional experience in a structured software engineering environment, working within collaborative teams where architecture, code quality, peer reviews, and shared system ownership are essential. - Strong software engineering and programming skills, with demonstrated experience building, maintaining, and deploying production-grade applications and systems. - Proven ability to take complex or experimental technical work and turn it into reliable, maintainable, and scalable production solutions. - Strong understanding of software architecture, engineering best practices, version control, code reviews, testing, and collaborative development workflows. - Experience with task management and development tools such as Jira or Linear, as well as Git-based workflows and CI/CD practices. - Genuine interest in artificial intelligence and machine learning, with familiarity or practical exposure to model training, inference pipelines, or collaboration with ML research teams. - Strong problem-solving and analytical skills, with the ability to work independently, navigate ambiguity, and make sound technical decisions in a fast-paced environment. - Excellent communication and collaboration skills, including the confidence to challenge technical decisions constructively and contribute to engineering standards. - Willingness to work primarily on applied engineering and deployment while remaining engaged with research and experimentation. - Ability to overlap with U.S. business hours while working remotely from Canada. Preferred qualifications: - Proficiency in Rust or a strong willingness and ability to develop expertise quickly, as the infrastructure is progressively transitioning toward Rust-native systems. - Experience with machine learning operations (MLOps), including experiment tracking, model versioning, feature stores, and model inference or serving frameworks. - Exposure to natural language processing (NLP) or text-based signal extraction in applied engineering environments. - Experience working alongside ML researchers, quantitative developers, or data science teams. - Familiarity with financial markets, trading systems, or a strong curiosity about financial technology and market dynamics. - Experience building monitoring, logging, and alerting infrastructure for production systems. ### Benefits - Competitive compensation: Attractive total compensation package with significant performance-based bonus potential. - Remote-first flexibility: Work remotely within a distributed international team, with flexible working hours and location. - In-person collaboration: Opportunities to participate in regular team sprints, retreats, and collaborative events. ### 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/lever%3Ajobgether%3A85c00f38-52e2-4375-aa50-68c5ddd4e1aa and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.