[All remote jobs](https://jobicy.com/jobs.md)[![Limbic logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/07/5d906349473305a0cb5a02dc5d4c74b3.png)](https://jobicy.com/company/limbic.md)[Limbic](https://jobicy.com/company/limbic.md) # Senior AI/ML Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/limbic.md)ShareRemote from[Canada](https://jobicy.com/job-region/canada.md)SalaryCAD 173k–192k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished22 Oct 2026Apply before21 Nov 2026Listing views55Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Limbic is seeking a senior AI/ML engineer to own conversational clinical-assessment AI used by US healthcare providers. The role focuses on safe, accurate LLM-based patient interactions, clinical prediction, treatment recommendations, and evaluation systems. The engineer will productionize ML models, build agent harnesses and simulation tooling, and help extend the platform to channels such as voice. This is a high-impact healthcare AI position requiring strong Python and TypeScript expertise, reinforcement learning experience, production architecture skills, and rigorous evaluation practices. Candidates must be authorized to work remotely from Canada and collaborate during North American business hours. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThe role combines senior-level ML systems engineering with safety-critical clinical AI, regulatory constraints, and direct ownership of production quality. Success requires deep LLM evaluation expertise, scalable architecture, and sound judgment in high-stakes patient-facing scenarios. ## Salary analysis Estimated compensation compared with the broader CA market for similar roles. Estimated job median (per year)Market rateC$182,500CA market range (per year)C$165k–C$210k0C$231.00k AI insightThe employer discloses a salary of CA$173,000–CA$192,000 per year, with a midpoint of CA$182,500 annually. Estimated Canadian market compensation for a senior AI/ML engineer working on production, safety-critical healthcare AI is approximately CA$165,000–CA$210,000 per year; the disclosed range is competitive within that estimate. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Large Language Models](https://jobicy.com/jobs?search_keywords=Large%20Language%20Models.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[TypeScript](https://jobicy.com/jobs?search_keywords=TypeScript.md)[LLM evaluation](https://jobicy.com/jobs?search_keywords=LLM%20evaluation.md)[AI agents](https://jobicy.com/jobs?search_keywords=AI%20agents.md)[Reinforcement Learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20Learning.md)[Clinical AI safety](https://jobicy.com/jobs?search_keywords=Clinical%20AI%20safety.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Machine Learning Architecture](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Architecture.md)[Statistical Analysis](https://jobicy.com/jobs?search_keywords=Statistical%20Analysis.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Cloud Computing](https://jobicy.com/jobs?search_keywords=Cloud%20Computing.md)[Containerization](https://jobicy.com/jobs?search_keywords=Containerization.md)[Git](https://jobicy.com/jobs?search_keywords=Git.md) Sample interview questionsHow would you design an evaluation framework for a conversational AI that conducts mental-health patient assessments?I would define a clinically reviewed rubric covering safety, risk escalation, questionnaire fidelity, factuality, empathy, treatment-routing accuracy, and resistance to prompt manipulation. I would combine curated labelled transcripts, simulated patient scenarios, adversarial tests, automated model-based checks, and clinician review. Releases would require threshold-based gates, regression testing by configuration, and monitoring of production outcomes. Describe how you would reduce unsafe AI responses without excessively increasing false positives. I would first segment failures by risk type and measure precision and recall for each safety control. I would tune layered defenses such as deterministic policy checks, specialized classifiers, prompt constraints, and escalation workflows, then validate changes against representative and adversarial evaluation sets. Human clinical review of borderline examples would help calibrate thresholds and prevent safe responses from being unnecessarily blocked. What considerations are important when productionizing an ML model for clinical prediction? I would prioritize data quality, reproducibility, versioned features and models, calibration, subgroup performance, monitoring for drift, and clear fallback behavior. The system should have traceable decisions, auditable evaluation evidence, secure handling of health data, and deployment controls that enable rollback. I would also ensure that predictions support clinician review rather than implying unsupported autonomous clinical decisions. How have you approached end-to-end ownership of an LLM product feature? I start by defining the user problem, risk boundaries, measurable success criteria, and a thin deliverable that can be safely shipped. I build instrumentation and an evaluation suite before broad release, collaborate with product and domain experts on edge cases, and monitor real-world quality after launch. I then use outcome data and failure analysis to prioritize iterative improvements. How would you architect reusable AI services for multiple healthcare partners with different workflows? I would separate a shared, versioned core assessment engine from configuration-driven partner workflows, questionnaires, routing rules, and integrations. Strong schemas, tenant isolation, audit logging, APIs, and configuration validation would make the platform composable while preserving safety controls. Each configuration would be tested through automated simulations and approval gates before it is exposed to patients. Opportunity details ## About this role. Senior AI/ML Engineer, remote in Canada: own the AI that assesses patients for US healthcare providers and gets them to the right mental health care faster. About Limbic Limbic’s vision is to ensure the highest quality therapy is accessible to everyone, everywhere. Limbic has supported more than 820,000 patients across the UK and the US. We are used in over 50% of NHS Talking Therapies services, which makes us the largest-ever deployment of generative AI for direct patient care, and our AI now delivers end-to-end therapy to patients in the US under the supervision of licensed clinicians. Safety and science come first. We are the only AI mental health chatbot with UKCA Class IIa medical device certification, and the first AI-led mental healthcare company selected for the FDA’s TEMPO pilot. Our results are published in leading scientific journals: in a randomized, double-blind trial published in Nature Medicine, our AI agents scored higher than licensed CBT therapists on a standard measure of therapy skill. The Role As a Senior AI/ML Engineer, you will take ownership of the AI in our clinical assessment product for US healthcare organizations. A patient describes what they are struggling with in a conversation with our AI, which gathers their history, administers standard clinical questionnaires such as PHQ-9 and GAD-7, detects risk and recommends a treatment program for the organization’s clinicians to review. Every organization configures it for its own services, so the AI has to be safe and accurate across many clinical setups, not just one. The approach is proven at scale in the NHS, where it increased referrals from minority groups by 29%, as published in Nature Medicine. You will work in North American hours alongside product, engineering and clinical colleagues, in close collaboration with our ML team in London. Our AI products are built on shared platforms for agents, evaluation and safety, so the systems you design are reused across Limbic. What You’ll Do * Own the quality of the conversational AI that runs patient assessments, from each step of the conversation to clinical prediction and treatment recommendation. * Improve the safety checks that screen every patient message and AI reply, covering risk, attempts to manipulate the AI, unsafe medical advice and clinically inappropriate responses, so they catch more problems without flagging good replies. * Productionize ML models that optimize product KPIs, including the accuracy of clinical predictions and how quickly patients reach the right care. * Build and maintain evaluation harnesses, simulated patient conversations and labelled transcript review, so every configuration can be tested before a patient sees it. * Extend our assessment AI to new channels, including the phone, so that patients who cannot or will not use a web form still have a way in. * Help turn our clinical AI into composable services that healthcare partners can integrate into their own patient journeys. * Build agents and tooling that automate product development workflows, including reliable testing and customer demos. * Raise the engineering bar through architecture, code review and mentorship, while keeping our AI continuously compliant with strict regulatory requirements. What You’ll Bring * You must meet one of the following educational and experience backgrounds: A PhD in computational science or an AI-related field, plus 2 years of experience in an ML engineering role. * OR A BSc/MSc, plus 4 years of experience in an ML engineering role. * Expertise in productizing LLM features and a strong background in eval-driven development. * Expert-level proficiency in Python and TypeScript/JavaScript. * Proven experience building an LLM agent harness. * Experience utilizing reinforcement learning. * Strong architectural skills with experience designing scalable, production-grade systems for machine learning applications. * A solid understanding of statistical principles in the context of LLM evaluation and user activity analytics. * Proficiency with version control systems (preferably Git) and databases (SQL or NoSQL). * Hands-on experience with a major cloud platform and containerization technologies. * A track record of end-to-end ownership: you scope work you can ship quickly, measure its impact, and keep a sense of urgency when the stakes are high. * A genuine interest in applying your technical skills to solve complex challenges in the mental healthcare space. * Ideal but not required: Experience with real-time voice AI, or with healthcare organizations as customers. * North American business hours (Eastern time preferred), with daily overlap with colleagues in Europe (UTC to UTC+3). * Based in Canada with the right to work there. Please note: Limbic cannot provide visa sponsorship in Canada. Benefits & Wellbeing * Salary: CA$173,000 – CA$192,000 per year. * Equity share options. * 25 days PTO. * Support with purchasing work-related books and materials. * Quarterly Life Days: Enjoy 4 paid days off per year (one each quarter) to use whenever you choose to relax, recharge, or take care of personal matters. * Mental Health Support: Access to dedicated mental health support services. We encourage women and individuals from diverse backgrounds to apply and join our team. We believe in creating an inclusive and supportive work environment where everyone can contribute their best. 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