Member, Technical Staff

Résumé du poste

Build and maintain technical pipelines that transform complex clinical standards into executable, verifiable decision artifacts. Collaborate with clinicians and technical leaders to ensure system reliability, traceability, and clinical fidelity through rigorous benchmarking and validation.

Détails du poste

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Member, Technical Staff based in Canada. The Member, Technical Staff will help build the technical infrastructure that turns complex clinical knowledge into executable, reliable AI systems. This is a highly technical opportunity at the intersection of machine learning, clinical AI, natural language processing, and formal methods. You will work on systems designed to transform clinical standards into deterministic, verifiable, and traceable decision artifacts. The role combines frontier AI research with hands-on production engineering, giving you the opportunity to take ideas from experiments and benchmarks into real-world clinical applications. You will work closely with technical leaders and clinicians to ensure systems are complete, correct, and evidence-bound. With significant ownership and a remote-first setup, this role offers the chance to make a meaningful impact in a small, ambitious, deeply technical environment. \n Accountabilities Build and improve the technical pipeline that transforms clinical standards into executable decision modules. Develop and iterate on representations capable of capturing complex clinical guideline logic, including rules, exceptions, algorithms, and relationships. Experiment with open-source and proprietary AI models, fine-tuning approaches, constrained decoding, and other advanced model techniques. Design and maintain rigorous benchmarks, datasets, evaluation metrics, and adversarial test cases to measure clinical fidelity and system performance. Investigate model failures systematically, turning unexpected behavior into falsifiable hypotheses and well-designed experiments. Develop deterministic validation systems using techniques such as static analysis, constraint solving, formal verification, and related methods. Translate successful research findings into robust approaches suitable for production deployment. Work directly with clinicians and technical leadership to validate outputs and ensure that generated decision artifacts are complete, correct, reliable, and traceable to their source evidence. Contribute to research direction, technical architecture, and product development within a small, highly autonomous team. Continuously investigate how modern AI techniques can be pushed further while maintaining the level of precision and reliability required in clinical environments. Requirements 3+ years of relevant research or industry experience in machine learning, artificial intelligence, clinical AI, NLP, formal methods, or a related technical field; final-year PhD candidates and postdoctoral researchers may also be considered. Strong technical foundation in machine learning, AI, NLP, formal methods, software engineering, or related disciplines. Demonstrated ability to design experiments and evaluation frameworks that rigorously test whether an AI system actually works. Experience developing datasets, benchmarks, metrics, validation methodologies, or adversarial evaluation approaches is highly valuable. Ability to investigate complex model behavior and translate failures into concrete, testable hypotheses. Familiarity with model fine-tuning, constrained decoding, or modern AI experimentation is an asset. Understanding of deterministic validation, static analysis, constraint solving, formal verification, or related techniques is valuable. Strong software development and systems-building capabilities, with the ability to move research concepts toward production-quality implementations. Exceptional analytical and problem-solving skills, with a rigorous and evidence-driven approach to technical decisions. Curiosity and intellectual independence, with a willingness to challenge assumptions and investigate difficult technical questions. Strong communication and collaboration skills, particularly when working with highly technical colleagues and clinical experts. Ability to thrive in a small, fast-moving, high-ownership environment where individual contributions can significantly influence research and product direction. Genuine interest in building AI systems where correctness, reliability, traceability, and real-world impact are critical. Benefits Competitive base salary with a meaningful equity package. Remote-first position available to candidates based in Canada. Periodic in-person team offsites. Opportunity to work directly with technical leadership, founders, and clinical experts. High level of ownership and significant influence over research and technical direction. Opportunity to work on frontier AI research with genuine clinical and real-world impact. Exposure to the full research-to-production lifecycle, from experiments and benchmarks to deployed systems. Opportunity to work at the intersection of AI, machine learning, clinical knowledge, NLP, and formal methods. Small, deeply technical environment designed for ambitious and intellectually curious professionals. Chance to help build infrastructure that makes complex clinical knowledge executable, verifiable, and traceable. \n 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! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1

Ce que vous ferez

Build and maintain technical pipelines that transform complex clinical standards into executable, verifiable decision artifacts. Collaborate with clinicians and technical leaders to ensure system reliability, traceability, and clinical fidelity through rigorous benchmarking and validation.

Exigences

Requires 3+ years of experience in machine learning, AI, NLP, or formal methods, with PhD candidates also considered. Candidates must possess strong software engineering skills and the ability to design rigorous evaluation frameworks for AI systems.

Avantages

• Competitive base salary • Equity package • Remote-first setup • Periodic in-person team offsites

Compétences indiquées

• Apprentissage automatique · Souhaitée
• Python · Souhaitée

Autres compétences pertinentes

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Machine learning
• Artificial intelligence
• Natural language processing
• Formal methods
• Software engineering
• Clinical AI
• Constraint solving
• Static analysis
• Model fine-tuning
• Constrained decoding
• Data evaluation
• System architecture
• Clinical guidelines
• Adversarial testing
• Python
• Research methodology
• Influencing Skills
• Intellectual Curiosity
• Influencing Without Authority
• General Data Protection Regulation (GDPR)
• AI Research
• Curiosity
• Data Privacy
• Technical Leadership
• Research
• Artificial Intelligence
• Algorithms
• Software Development
• Static Program Analysis
• Communication
• Information Privacy
• Deterministic Methods
• Executable
• Experimentation
• Traceability
• Formal Verification
• Problem Solving
• Machine Learning
• New Product Development
• Natural Language Processing (NLP)
• Performance Metric
• Production Engineering
• Software Engineering
• Testability
• Collaboration
• Reliability
• Formal Methods
• Artificial Intelligence Infrastructure

Domaines d’emploi

• Technology
• Science & Research
• Software
• Healthcare
• Data & Analytics
• Member of Technical Staff
• Artificial Intelligence Engineer (General)
• Software Developers