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Machine Learning Engineer - Job Opportunity at TekFormix

Toronto, Canada
Full-time
Mid-level
Posted: June 7, 2025
On-site
CAD 80,000-150,000 per year

Key Responsibilities

Lead the design and implementation of machine learning models that directly solve complex business challenges, driving innovation and competitive advantage through data-driven solutions
Conduct comprehensive analysis of large-scale datasets to extract actionable insights that enhance model performance and inform strategic business decisions
Facilitate cross-functional collaboration to seamlessly integrate machine learning capabilities into existing product ecosystems, ensuring scalable and maintainable solutions
Maintain technological leadership by staying current with AI and machine learning advancements, positioning the organization at the forefront of industry innovation
Drive performance optimization of algorithms for production environments, ensuring scalability and reliability that supports business growth objectives
Establish comprehensive documentation standards and knowledge-sharing protocols that enhance team capability and organizational learning

Requirements

Education

Bachelor's or Master's degree in Computer Science, Data Science, or a related field

Experience

Proven experience with machine learning frameworks and libraries

Required Skills

TensorFlow PyTorch Python R Java AWS Hadoop Spark problem-solving skills collaborative teamwork
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Sauge AI Market Intelligence

Industry Trends

The machine learning engineering field is experiencing unprecedented growth driven by the widespread adoption of AI across industries, with companies increasingly recognizing ML as a core competitive differentiator rather than a nice-to-have technology. This shift is creating substantial demand for engineers who can bridge the gap between research and production systems. Cloud-native ML deployment has become the dominant paradigm, with organizations prioritizing engineers who can leverage platforms like AWS, Google Cloud, and Azure to build scalable, cost-effective ML solutions. The emphasis has shifted from on-premises solutions to cloud-first architectures that enable rapid scaling and experimentation. The MLOps movement is revolutionizing how machine learning systems are developed and maintained, with companies seeking engineers who understand the full lifecycle of ML systems including monitoring, versioning, and automated deployment pipelines. This represents a maturation of the field from experimental to production-focused engineering discipline.

Salary Evaluation

The offered salary range of CAD 80,000-150,000 is competitive for the Toronto market, positioning slightly above average for mid-level ML engineers. The wide range suggests flexibility based on experience level, with the upper end reflecting the premium placed on ML expertise in the Canadian tech hub. This range aligns well with Toronto's cost of living and the city's position as a major AI research center.

Role Significance

Based on the collaborative nature described, this role likely operates within a data science team of 5-8 members, working closely with product managers, software engineers, and business stakeholders. The emphasis on documentation and knowledge sharing suggests a growing team environment where institutional knowledge preservation is crucial.
This role represents a mid-level individual contributor position with significant technical ownership and cross-functional influence. The responsibilities suggest autonomy in technical decision-making while requiring collaboration skills to work effectively across teams. The role appears to balance hands-on technical work with strategic thinking about model implementation and optimization.

Key Projects

Development of recommendation systems and personalization engines that directly impact user engagement and revenue generation Implementation of predictive analytics solutions for business forecasting, risk assessment, and operational optimization Creation of automated decision-making systems that enhance operational efficiency and reduce manual intervention requirements Integration of natural language processing capabilities to improve customer experience and internal productivity tools

Success Factors

Technical excellence in implementing production-ready ML systems that balance accuracy with computational efficiency, demonstrating ability to translate research concepts into scalable business solutions Strong communication skills to effectively bridge technical and business stakeholders, ensuring ML initiatives align with organizational objectives and deliver measurable value Adaptability to rapidly evolving ML landscape, showing continuous learning mindset and ability to evaluate and integrate new technologies and methodologies as they emerge Systems thinking approach to understand how ML components fit within broader technical architecture and business processes, ensuring sustainable and maintainable solutions

Market Demand

Demand for machine learning engineers remains exceptionally high, with supply constraints creating a candidate-driven market. Toronto's position as a global AI hub, anchored by institutions like Vector Institute and strong tech ecosystem, ensures robust demand that typically exceeds available talent supply.

Important Skills

Critical Skills

Python programming expertise is fundamental as it serves as the primary language for ML development, data processing, and integration with production systems. Proficiency directly impacts development velocity and code quality in collaborative environments. TensorFlow and PyTorch knowledge represents essential technical competency for implementing modern ML models, with these frameworks being industry standards that enable efficient model development, training, and deployment at scale. Cloud platform experience, particularly AWS, is crucial for modern ML engineering as it enables scalable model deployment, cost-effective computing resources, and integration with broader cloud-native architectures that most companies now employ. Problem-solving skills are critical as ML engineering involves complex technical challenges that require analytical thinking, debugging capabilities, and the ability to iterate on solutions while balancing multiple constraints including performance, accuracy, and business requirements.

Beneficial Skills

Experience with MLOps tools and practices would be valuable as the industry increasingly emphasizes reliable ML system deployment and monitoring, representing a natural career advancement area Knowledge of additional cloud platforms like Google Cloud or Azure would provide flexibility and broader market opportunities, as multi-cloud strategies become more common Understanding of software engineering best practices including testing, version control, and CI/CD specifically for ML systems would differentiate candidates and align with industry maturation trends Business acumen and domain expertise in specific industries would enhance the ability to translate business requirements into effective ML solutions and communicate value to stakeholders

Unique Aspects

The role uniquely combines traditional ML engineering with explicit focus on integration into existing products, suggesting a mature product environment rather than experimental or research-focused work
Strong emphasis on optimization for production environments indicates the company has moved beyond proof-of-concept phase and is focused on delivering scalable, reliable ML solutions to real users
The requirement for big data technologies like Hadoop and Spark suggests the company works with substantial data volumes, indicating either large-scale operations or serving high-volume clients
The collaborative cross-functional aspect is particularly emphasized, suggesting ML engineers are integral to product development rather than operating in isolation

Career Growth

Career progression typically occurs within 2-4 years for high-performing engineers, with acceleration possible through demonstrated impact on business outcomes and technical leadership within projects. The Toronto market's competitive nature may accelerate advancement opportunities.

Potential Next Roles

Senior Machine Learning Engineer with expanded technical leadership responsibilities and mentorship opportunities Machine Learning Team Lead or Engineering Manager, transitioning into people management and strategic technical planning Principal ML Engineer or Staff Engineer roles focusing on architectural decisions and cross-team technical leadership Specialized roles such as ML Infrastructure Engineer, MLOps Engineer, or Research Scientist depending on interest areas

Company Overview

TekFormix

TekFormix appears to be positioned as a technology company focused on software development solutions, with machine learning being integrated into their core product offerings. The company's emphasis on innovative technology solutions suggests they are likely serving enterprise clients or developing B2B software products that leverage AI capabilities.

As a company actively hiring ML engineers and emphasizing innovation, TekFormix appears to be in a growth phase, positioning itself to compete in the competitive Toronto tech market. Their focus on ML integration suggests they recognize the strategic importance of AI capabilities in maintaining competitive advantage.
Operating in Toronto positions TekFormix within one of North America's most vibrant AI ecosystems, providing access to world-class talent from universities like University of Toronto and strong networking opportunities within the Vector Institute community. This location offers significant advantages for AI talent acquisition and partnership opportunities.
The emphasis on collaboration, knowledge sharing, and staying current with latest technologies suggests a learning-oriented culture that values continuous improvement and cross-functional teamwork. The focus on documentation and team knowledge sharing indicates a mature engineering culture that prioritizes sustainable development practices.
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  • Salary estimates and market demand analysis
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  • Critical success factors and key skills
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