Member, Technical Staff

JobgetherClinical AI

Based in CanadaFull-TimeMiddle

SalaryCompetitive base salary with a meaningful equity package.

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Job Details

Experience: 3+ years of relevant research or industry experience
Required Skills: Artificial IntelligenceMachine LearningSoftware EngineeringNLP

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.

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.

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