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Success Matcher Recruitment

Member, Technical Staff

Toronto, ON, Canada

Full-time

Within 25 km of Vaughan · About 16.5 km from the city centre

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Job details

Job type: Full-time
Apply by: Dec 9, 2026
Posted: Sep 11, 2026

Original job description

Build the infrastructure that makes clinical knowledge executable.

We’re working with an early-stage, deeply technical clinical AI company building a fundamentally different approach to how AI can be used in healthcare.

Their core technology transforms clinical standards - including prose, tables, algorithms, exceptions, and cross-references - into executable, evidence-bound decision artifacts that are deterministic, verifiable, and traceable to their source evidence.

The technology is already live in clinical production, meaning this is an opportunity to work on frontier AI research with genuine real-world impact - not another research project sitting in a demo environment.

WHY THIS IS A GREAT OPPORTUNITY

Frontier AI + real-world clinical impact: Research how far modern models can be pushed while ensuring outputs meet the precision required in healthcare.
Research + production: Design experiments, build benchmarks, investigate model failures, and ship successful approaches into a production pipeline.
High ownership: Join a very small, highly technical team where your work can materially influence the product and research direction.
Work directly with founders and clinicians: Collaborate closely with the people building and validating the technology.
Remote-first: Work remotely from the US or Canada, with periodic in-person offsites.
Meaningful equity: Competitive equity package alongside a strong base salary.

WHAT YOU'LL DO

Build the pipeline that converts clinical standards into executable modules.
Develop and iterate on representations for clinical guideline logic.
Experiment with open/closed models, fine-tuning, and constrained decoding.
Design benchmarks, datasets, metrics, and adversarial cases for clinical fidelity.
Turn model failures into falsifiable hypotheses and experiments.
Develop deterministic validators using static analysis, constraint solving, and formal methods.
Work directly with clinicians and technical leadership to ensure outputs are complete, correct, and traceable.

WHAT WE'RE LOOKING FOR

You could be a strong fit if you have 3+ years of research or industry experience, or are a final-year PhD/postdoc, particularly in areas involving ML, AI, clinical AI, NLP, formal methods, or related fields.

The ideal candidate is someone who enjoys asking: “How do we know this actually works?”- and then designing the experiment, benchmark, or system needed to prove it.

Interested in building AI systems where correctness actually matters? Apply to learn more.

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More about this job

Responsibilities

The Member, Technical Staff at Success Matcher Recruitment will build the pipeline that turns clinical guidelines into working software modules. They will design experiments, benchmarks and validators to prove the AI outputs are correct, working closely with clinicians and founders.

Requirements

Candidates need 3+ years of research or industry experience, or be a final-year PhD or postdoc, ideally in machine learning, clinical AI, NLP or formal methods. Success Matcher Recruitment wants someone who asks how to prove a system works and then designs the test to show it.

Skills

Machine learning
Natural language processing
Fine-tuning open and closed models
Constrained decoding
Benchmark and dataset design
Experiment design
Failure analysis
Formal methods
Static analysis
Constraint solving
Deterministic validation
Clinical guideline representation
Python
Research methodology
Clinical collaboration

Benefits

Meaningful equity
Remote-first work
Periodic in-person offsites

Keywords

clinical AI
machine learning
NLP
formal methods
constrained decoding
fine-tuning
LLM
benchmark design
clinical guidelines
static analysis
constraint solving
model evaluation
health informatics
remote
Canada
United States
equity

Perks

Stock or equity

Languages

English

Seniority

Mid level

Fields of study

Machine learning
Artificial intelligence
Natural language processing
Formal methods

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