Senior AI/ML Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior AI/ML Engineer based in Canada.
As a Senior AI/ML Engineer, you will shape the development of advanced conversational AI systems that help patients access the right mental healthcare faster and more effectively. You will take end-to-end ownership of the AI powering clinical assessments, from patient conversations and risk detection to clinical predictions and treatment recommendations. Working at the intersection of machine learning, software engineering, and healthcare, you will build safe, reliable, and scalable AI solutions for healthcare providers across the United States. You will lead improvements in large language model (LLM) performance, evaluation frameworks, and clinical safety mechanisms while translating research into production-ready systems. Collaborating with product, engineering, and clinical experts across North America and Europe, you will contribute to shared AI platforms and innovative patient engagement experiences. This remote opportunity offers the chance to apply cutting-edge AI technologies to meaningful clinical challenges while helping expand access to high-quality mental healthcare.
Accountabilities
Own the quality, reliability, and performance of conversational AI systems used in clinical assessments, covering the full patient journey from initial conversations to clinical predictions and treatment recommendations.
Design, develop, and optimize LLM-powered features that gather patient histories, administer standardized clinical questionnaires, identify potential risks, and recommend appropriate treatment pathways for clinical review.
Strengthen AI safety mechanisms that evaluate patient messages and model responses, detecting clinical risks, adversarial inputs, inappropriate medical advice, and potentially harmful outputs while minimizing unnecessary alerts.
Productionize machine learning models that improve key product metrics, including clinical prediction accuracy, patient experience, and the speed at which patients are connected with suitable care.
Build and maintain comprehensive evaluation frameworks, including automated testing harnesses, simulated patient conversations, and labeled transcript reviews, to validate AI performance across different healthcare configurations.
Ensure AI systems remain accurate, safe, and consistent across multiple healthcare providers, clinical workflows, and service configurations.
Extend conversational assessment capabilities to additional channels, including real-time voice and telephone interactions, to improve accessibility for patients who cannot or prefer not to use web-based tools.
Develop modular AI services that healthcare partners can integrate into their own patient journeys, enabling flexible implementation and broader adoption.
Build AI agents and developer tools that automate product development workflows, improve testing reliability, and streamline the creation of customer demonstrations.
Collaborate with product managers, engineers, clinical specialists, and machine learning colleagues across North America and Europe to translate clinical and business requirements into effective technical solutions.
Contribute to shared platforms for AI agents, evaluation, and safety, ensuring that improvements can be reused across multiple products and applications.
Establish and promote engineering best practices through system architecture, code reviews, technical documentation, and mentorship of fellow engineers.
Maintain rigorous standards for clinical safety, quality assurance, and regulatory compliance throughout the AI development lifecycle.
Monitor system performance and evaluate the impact of new features, using statistical analysis and real-world usage data to guide continuous improvement.
Requirements
Education and experience: Either a PhD in computational science or an AI-related discipline with at least 2 years of experience in a machine learning engineering role, or a bachelor's or master's degree with at least 4 years of machine learning engineering experience.
Expert-level proficiency in Python and TypeScript or JavaScript, with demonstrated experience building and maintaining production-grade AI applications.
Strong expertise in deploying LLM-powered features into production environments, supported by a thorough understanding of evaluation-driven development and systematic model assessment.
Proven experience designing and building LLM agent harnesses, including the infrastructure, orchestration, and evaluation mechanisms required to develop reliable AI agents.
Practical experience applying reinforcement learning techniques to machine learning systems or AI-driven applications.
Strong software architecture skills, with experience designing scalable, maintainable, and reliable production systems for machine learning applications.
Solid understanding of statistical principles and their application to LLM evaluation, model performance measurement, experimentation, and user activity analytics.
Proficiency with version control systems, preferably Git, and experience working with relational or non-relational databases using SQL or NoSQL technologies.
Hands-on experience with a major cloud platform and containerization technologies for deploying and operating production services.
Demonstrated ability to take end-to-end ownership of technical initiatives, from scoping and implementation to deployment, measurement, and iterative improvement.
Strong analytical and problem-solving skills, sound technical judgment, and the ability to prioritize effectively and deliver high-quality solutions in a fast-paced environment.
Excellent communication and collaboration skills, with the ability to work effectively across engineering, product, research, and clinical teams in a distributed organization.
Genuine interest in applying AI and machine learning to complex mental healthcare challenges, with a strong appreciation for patient safety and responsible AI development.
Ability to work during North American business hours, preferably Eastern Time, with daily collaboration overlap with colleagues in European time zones ranging from UTC to UTC+3.
Current residence in Canada and legal authorization to work in the country. Visa sponsorship is not available for this position.
The Following Qualifications Are Considered An Asset
Experience developing real-time voice AI systems, conversational speech interfaces, or telephone-based AI applications.
Previous experience working with healthcare organizations, clinical service providers, or healthcare technology customers.
Familiarity with regulated AI applications, clinical assessment workflows, or the development of safety-critical machine learning systems.
Experience designing evaluation systems for conversational agents operating across multiple configurations and real-world use cases.
A strong interest in advancing equitable access to mental healthcare through responsible and evidence-based AI innovation.
Benefits
Competitive salary: Annual compensation ranging from CAD $173,000 to $192,000.
Equity participation: Access to equity share options, providing an opportunity to participate in the organization's long-term growth, subject to the applicable plan.
Generous paid time off: 25 days of paid time off annually to support rest, recovery, and work-life balance.
Additional personal leave: Four paid Life Days per year, with one day available each quarter for relaxation, personal commitments, or recharging.
Mental health support: Access to dedicated mental health support services.
Professional development resources: Financial support for purchasing work-related books and learning materials.
Remote work flexibility: A fully remote position based in Canada, offering flexibility while working with an international team.
International collaboration: Opportunities to work closely with AI, engineering, product, and clinical specialists across North America and Europe.
Meaningful work: The opportunity to develop AI systems that help patients access appropriate mental healthcare more efficiently and support better clinical experiences.
Inclusive working environment: A commitment to diversity, inclusion, and creating a supportive workplace where people from different backgrounds can contribute and thrive.
Technical innovation: The opportunity to work on advanced LLMs, AI agents, machine learning evaluation, and clinical safety systems in a rapidly evolving field.
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!
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