Applied ML Engineer - Model R&D

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.
Professional development: Dedicated learning and conference budgets to support continuous skill development and technical growth.
Home-office support: Hardware and home-office stipend to help create an effective remote working environment.
Comprehensive healthcare: Medical, dental, and vision coverage, alongside life insurance.
Flexible vacation: Open vacation policy designed to support work-life balance.
Parental support: 16 weeks of fully paid parental leave, with additional support during the return-to-work transition.
Retirement benefits: Retirement savings plan with employer matching.
High-impact environment: Opportunities to experiment, build innovative AI/ML systems, and contribute directly to business and performance outcomes.
Autonomy and ownership: A collaborative, entrepreneurial culture that encourages initiative, continuous learning, and end-to-end responsibility.