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Machine Learning Engineer Biotech Jobs in Toronto, ON

Thri5 Inc.

AI / Machine Learning Engineer

Toronto, ON · On-site

Your Role As an AI / Machine Learning Engineer at Thri5, you'll help build the agent layer that powers our System of Actions. You'll design and implement multi-agent Co-pilot systems that orchestrate ...

Autodesk

Machine Learning Ops Developer

Toronto, ON

... Engineer to join our AI/ML Platform team. This role is pivotal in ensuring the smooth operationalization of machine learning models and the overall efficiency of our next-generation AI/ML platform ...

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Machine Learning Engineer Biotech information

What does a Machine Learning Engineer do in the biotech industry?

A Machine Learning Engineer in biotech applies advanced algorithms and data analysis techniques to solve biological and medical problems. They work with large datasets such as genomic sequences, medical images, or clinical records to develop predictive models, automate data analysis, and uncover insights that can accelerate drug discovery, diagnostics, and personalized medicine. Their work often involves close collaboration with biologists, data scientists, and software engineers to create tools and solutions that improve healthcare outcomes. Machine Learning Engineers in this field need a strong background in both computational methods and biological sciences.

How do Machine Learning Engineers in biotech typically collaborate with research scientists and domain experts?

Machine Learning Engineers in biotech often work closely with research scientists and domain experts to translate complex biological problems into data-driven solutions. This collaboration involves regular meetings to understand experimental data, refine project goals, and iterate on model development based on domain feedback. Engineers are expected to communicate technical concepts clearly, adapt models to fit scientific needs, and help validate results alongside laboratory teams. This interdisciplinary environment fosters innovation but also requires flexibility and strong communication skills.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer in Biotech, and why are they important?

To thrive as a Machine Learning Engineer in Biotech, you need a solid background in computer science, statistics, and biology, often with an advanced degree in a related field. Experience with programming languages such as Python or R, machine learning frameworks like TensorFlow or PyTorch, and familiarity with bioinformatics tools are typically required. Strong problem-solving, communication, and interdisciplinary collaboration skills set standout candidates apart. These capabilities are crucial for developing effective models that drive scientific innovation and advance biotechnological research.

What is the difference between Machine Learning Engineer Biotech vs Data Scientist Biotech?

AspectMachine Learning Engineer BiotechData Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related; knowledge of ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops ML models, coding, deploying algorithms in biotech R&DAnalyzes biological data, interprets results, creates reports
Employer & Industry UsageBiotech firms, pharma companies, research labsBiotech companies, healthcare, research institutions

While both roles work with biological data, Machine Learning Engineers focus on developing and deploying ML algorithms, whereas Data Scientists analyze and interpret biological datasets to inform research and decision-making in biotech settings.

What are the most commonly searched types of Machine Learning Engineer Biotech jobs in Toronto, ON? The most popular types of Machine Learning Engineer Biotech jobs in Toronto, ON are:
What are popular job titles related to Machine Learning Engineer Biotech jobs in Toronto, ON? For Machine Learning Engineer Biotech jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Biotech jobs in Toronto, ON look for? The top searched job categories for Machine Learning Engineer Biotech jobs in Toronto, ON are:
Machine Learning Engineer Biotech jobs near you
Infographic showing various Machine Learning Engineer Biotech job openings in Toronto, ON as of June 2026, with employment types broken down into 100% Full Time. Highlights an 76% In-person, 4% Hybrid, and 20% Remote job distribution.

Machine Learning Engineer - Enterprise

Boson AI

Toronto, ON

CA$150K - CA$400K/yr

Full-time

Posted 22 days ago

Job description

About Boson AI: At Boson AI, we are not just building AI solutions; we are pioneering the future of enterprise AI. Driven by a passion for cutting-edge AI research, particularly in the transformative areas of large language models and agentic systems, our mission is to tackle the most complex real-world problems for businesses and unlock significant value. We are a dynamic and collaborative team of researchers and engineers who thrive on pushing the boundaries of what's possible, dedicated to delivering high-quality, reliable products that seamlessly integrate into the fabric of enterprise workflows and set new industry standards.

About the Role: We are seeking a skilled, detail-oriented, and passionate Machine Learning Engineer to join our enterprise team. In this pivotal role, you will be at the forefront of developing and deploying groundbreaking AI solutions. This involves integrating advanced language/voice/vision models, mastering fine-tuning techniques, building sophisticated workflows and platforms, and pioneering innovative agentic approaches. You will immerse yourself in challenging problems that demand a deep understanding of model behavior, meticulous implementation, and an unwavering commitment to quality and reliability in enterprise environments. A key and exciting aspect of this role is contributing to the architecture and implementation of intelligent systems where AI agents can perform complex tasks autonomously, interacting with diverse data sources and tools, as we collectively move towards building truly cohesive and powerful AI capabilities for our clients.
Responsibilities
  • Deliver solutions end to end that meet the needs of our customers - understanding user pain points, scoping product specs, and designing and building LLM-powered software.
  • Benchmark the model, and help write evals for customers to identify model weaknesses.
  • Develop and deploy modern search systems (e.g., RAG, DeepSearch) to enhance model performance, grounding, and the ability to utilize enterprise-specific knowledge.
  • Implement and optimize techniques for fine-tuning and align large models on domain-specific data.
  • Ensure the quality, reliability, security, and scalability of models and agentic systems through meticulous attention to detail, diligent execution, and continuous monitoring in demanding enterprise settings.
  • Integrate individual AI components into a scalable platform.
Qualifications
  • Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field, or equivalent practical experience.
  • Strong contribution record on GitHub. Please include your GitHub link in your application.
  • Experience working with large language or multimodal models and their applications.
  • Experience implementing and working with search systems.
  • Proven ability to pay close attention to detail and prioritize quality, reliability, and security in technical work.
  • Proficiency in programming languages (e.g., Python, Rust, TypeScript or Go) and relevant ML frameworks (e.g., PyTorch, JAX).
  • Demonstrated ability to design, chain, or orchestrate multiple models (especially LLMs) to create multi-step pipelines or workflows for task automation.
Bonus Points
  • Experience developing or contributing to agentic AI products or systems.
  • Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices.
  • Familiarity with distributed training and inference techniques.
  • Experience with system design, API development, and building scalable infrastructure for deploying and managing AI models or agentic systems.
  • Understanding of enterprise software integration patterns and data security considerations.
  • Solid understanding of HTTP protocol and real-time communication protocols (e.g., WebRTC) for voice AI. 
  • Excellent problem solving skills.
  • Ability to work independently and drive projects forward in a fast-paced environment
$150,000 - $400,000 a year
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.
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