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Flexible Remote Machine Learning Engineer Jobs in Toronto, ON

Levio

ML/AI Engineer

Toronto, ON · On-site +1

CA$110K - CA$150K/yr

The ML / AI Engineer design, build, deploy, and operate production-grade machine learning and ... The role will be remote. Why Join Levio? * Work on complex,high impactdigital transformation ...

Moneris

Senior Manager, Data Engineering

Toronto, ON · On-site +1

CA$142K - CA$177K/yr

Enable DataOps and MLOps practices, including feature engineering pipelines and machine learning ... Comprehensive Total Rewards Program, including performance-based bonuses, flexible benefits ...

Flinks

Senior Data Engineer

Toronto, ON · Remote

Machine Learning Platform Exposure: Experience supporting machine learning workflows, feature ... Health & Dental coverage as of Day 1Flexible Paid Time Off (FTO)Remote work environment with ...

YuJa

AI Engineer

Toronto, ON · On-site +1

CA$90K - CA$100K/yr

About the Role * Design, develop, and deploy machine learning and deep learning models ... Work-life balance including flexible work hours * Paid sick days Team Engagement: * Fun activities ...

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All JobsMachine Learning Engineer JobsFlexible Remote Machine Learning Engineer JobsFlexible Remote Machine Learning Engineer Jobs in Toronto, ON

Flexible Remote Machine Learning Engineer information

How does a flexible remote work arrangement impact collaboration and project delivery for machine learning engineers?

In a flexible remote setting, Machine Learning Engineers often rely on digital collaboration tools to communicate with team members and manage projects. This setup allows for asynchronous work, enabling engineers to focus deeply on model development and data analysis without constant interruptions. However, it also means proactively scheduling check-ins and maintaining clear documentation are crucial to ensure alignment across distributed teams. While remote work offers autonomy and work-life balance, successful engineers build strong communication habits to keep projects on track and foster effective collaboration with data scientists, product managers, and software engineers.

What is a flexible remote machine learning engineer?

A Flexible Remote Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models while working remotely, often with flexible hours. They use programming, data analysis, and statistical skills to create algorithms that solve real-world problems, collaborating with teams through digital communication tools. This role allows for a better work-life balance and can be performed from anywhere with a reliable internet connection. Flexible remote positions are especially popular in the tech industry, where project-based work and results matter more than strict office hours.

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

AspectFlexible Remote Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, ML, or related fields; experience with ML frameworksBachelor's or higher in CS, Statistics, or related fields; proficiency in data analysis
Work EnvironmentRemote, collaborative teams, project-basedRemote or on-site, data analysis-focused
Industry UsageTech, finance, healthcare, e-commerceTech, marketing, finance, research
Common Search IntentRoles involving ML model development and deploymentRoles focused on data analysis and insights

The main difference is that a Flexible Remote Machine Learning Engineer primarily develops and deploys machine learning models, while a Data Scientist focuses on analyzing data to generate insights. Both roles often require similar educational backgrounds and can be remote, but their core responsibilities differ in application and focus.

What are the key skills and qualifications needed to thrive as a flexible remote machine learning engineer?

To thrive as a Flexible Remote Machine Learning Engineer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and typically a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, cloud platforms (AWS, GCP, or Azure), and experience with data pipelines are essential, and certifications in machine learning or cloud technologies can be advantageous. Excellent communication, self-motivation, and time management skills help you collaborate effectively and stay productive in a remote, flexible work environment. These skills ensure you can independently deliver high-quality ML solutions, maintain clear team communication, and adapt to evolving project requirements.
What are popular job titles related to Flexible Remote Machine Learning Engineer jobs in Toronto, ON? For Flexible Remote Machine Learning Engineer jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching Flexible Remote Machine Learning Engineer jobs in Toronto, ON look for? The top searched job categories for Flexible Remote Machine Learning Engineer jobs in Toronto, ON are:
Flexible Remote Machine Learning Engineer jobs near you
Infographic showing various Flexible Remote Machine Learning Engineer job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 67% Full Time, 29% Part Time, and 3% Contract. Highlights an 86% Physical, 1% Hybrid, and 13% Remote job distribution.

Forward Deployed Engineer (Data, ML & AI)

Carbon60

Toronto, ON • Remote

Other

Retirement

Posted 4 days ago

Job description

Salary: 160000-180000

Forward Deployed Engineer (Data, ML & AI)

Location: Remote Timezone: NA/Eastern Type: Full-time Experience: 10+ Years


Role Overview

This position requires a Forward Deployed Engineer (FDE) specializing in Data, Machine Learning, and AI to embed directly within customer environments. You will serve as the primary technical authority transforming complex data challenges and operational bottlenecks into production-grade data pipelines, machine learning systems, and agentic AI solutions.

The role is heavily customer-facing: you will work alongside client business units and engineering teams to rapidly assess legacy data estates, build scalable modern data platforms, and deploy custom GenAI/LLM workflows that drive measurable business velocity. This is a high-ownership, hands-on role where you will leverage Spec-Driven Development (SDD) and AI-assisted workflows to build, refactor, and demonstrate immediate value directly where the customer operates.


Key Responsibilities

  • Customer Embedding & Data Delivery: Deploy directly into customer environments to understand their domain logic, underlying data pipelines, and architectural constraints. Own end-to-end deliveryfrom data discovery and schema design to pipeline deployment and model integrationbuilding trust as the customer's lead technical partner.
  • Data Platform & Architectural Modernization: Partner with client business leaders to evaluate legacy technology estates (e.g., monolithic SQL databases, or unmaintained ETL jobs). Lead engineering efforts to refactor legacy data setups into modern lakehouses, event-driven streaming systems, and scalable vector/graph databases.
  • Spec-Driven Development (SDD) for Data & AI: Apply a "spec-first" engineering workflow using Generative AI tools. Write structured specifications (data models, API schemas, transformations, and evaluation metrics) that instruct AI agents to generate production data models, PySpark jobs, data pipelines, and test suites.
  • Agentic AI & LLMOps Implementation: Architect and deploy GenAI workflows, Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents using frameworks such as LangChain, LlamaIndex, or DSPy. Establish robust evaluation frameworks (Evals) for model accuracy, latency, and hallucination control.
  • Production MLOps & Orchestration: Build, deploy, and maintain robust ML training and inference pipelines using tools like MLflow, Kubeflow, Airflow, or Dagster. Ensure continuous integration/continuous deployment (CI/CD) for models and data workflows.
  • Polyglot Data Engineering: Design and audit production code across data-centric languages and frameworks (Python, SQL, Scala, Go, Rust, or TypeScript) based on speed, concurrency, and memory requirements.
  • Client Enablement & Knowledge Transfer: Elevate customer teams by establishing reusable agentic development patterns, modern MLOps practices, data reliability frameworks, and SDD methodologies so systems remain maintainable long after deployment.


Requirements

  • 10+ Years of Experience: Proven track record as a Principal Data Engineer, Lead ML Engineer, or Enterprise Data Architect building and scaling distributed data and ML platforms.
  • Customer-Facing Aptitude: Strong executive presence and communication skills to interface directly with technical teams and business stakeholders under pressure.
  • Data & ML Engineering Depth:
    • Data Infrastructure: Mastery of distributed computing (AWS Glue, Apache Spark, Databricks), modern data warehouses (Redshift, Snowflake, modeling tools (dbt), and data orchestration (Airflow, etc)
    • AI/ML & Vector Architecture: Hands-on experience fine-tuning, evaluating, and deploying LLMs, embedding models, and vector stores
    • Polyglot & Framework Proficiency: Advanced proficiency in Python and complex SQL, plus fluency in at least two other languages used in modern backend/data systems (e.g., Scala, Go, Rust, TypeScript).
  • Generative AI & SDD Experience: Demonstrated skill in using natural language and structured specs to guide AI tools (Claude Code, Cursor, Copilot) in generating data pipelines, schemas, and API adapters.
  • Cloud & Infrastructure: Hands-on experience with cloud-native data services on AWS or Azure, containerization (Docker, Kubernetes), and Infrastructure as Code (Terraform).
  • Willingness to Travel: Comfort with occasional travel to customer sites as needed.


Preferred Qualifications

  • Prior experience in a Forward Deployed Engineer, Data Architect, or technical consulting/professional services role.
  • Experience migrating legacy, on-premise data warehouses or legacy Hadoop estates to modern cloud lakehouses.
  • Deep understanding of data governance, security compliance (HIPAA, SOC2, GDPR), and privacy-preserving machine learning.


Compensation & Perks

  • Competitive compensation package (160K - 180K CAD / year)
  • Retirement Savings Matching Program (RRSP)
  • Access to the latest tech
  • Partnership with Perkopolis Discounts

Flexibility & Time Off

  • Remote first work environment
  • Flexible work hours & location
  • Paid parental leave options

Health & Wellness

  • Employer paid health & dental premiums
  • GreenShield+ Counselling Mental Health
  • $500 in Health Care Spending Account annually

Growth & Development

  • Peer recognition rewards


As an employer, OpsGuru, a Carbon60 Company, recognizes the importance of balancing our careers with other aspects of our lives, and our culture reflects this ethos - from flexible work hours to health and wellness incentives and having fun along the way. We look for people who thrive in an environment of accountability and at times ambiguity as we adapt and grow our business.

OpsGuru is an equal-opportunity employer. We welcome and encourage applications from people with all levels of ability. Accommodations are available on request for candidates taking part in all aspects of the selection process. We thank all applicants for their interest in this exciting opportunity.


Only candidates that meet the qualifications will be contacted for an interview.