Member of Technical Staff, Compiler

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Develop the pipeline that transforms clinical source materials into evidence-based, actionable decision artifacts.

• Create intermediate representations for various populations, applicability, derived facts, temporal conditions, exclusions, precedence, and recommendations.

• Establish benchmarks and adversarial cases to assess source fidelity, logical completeness, elicitation quality, and recommendation accuracy.

• Translate model failures into hypotheses, experiments, modifications for training or inference, and enhancements in system design.

• Construct deterministic validators utilizing static analysis, constraint solving, formal methods, or other appropriate techniques.

• Clearly articulate ambiguity, contradictions, and gaps in source information.

• Successfully transition research into production alongside clinicians and engineers.

⛳️ Requirements

• Over 3 years of experience delivering machine learning or related systems in a production environment.

• Proven track record of outstanding technical contributions in fields such as machine learning, compilers, program synthesis, formal methods, natural language processing, document intelligence, evaluation, or related disciplines.

• Strong foundational knowledge in Python and software engineering principles.

• Proficient in PyTorch and the Transformers ecosystem for production use.

• Capable of transforming exploratory projects into clean and reliable systems.

• Skilled at formulating falsifiable hypotheses, designing experiments, interpreting noisy evidence, and drawing conclusions.

• Detail-oriented regarding specifications, edge cases, completeness, and supported outputs.

• Able to attain enough clinical fluency to engage effectively with domain experts.

• Excellent technical writing abilities, with a readiness to address uncertainty, unsuccessful strategies, and opposing evidence.

• Must be eligible to work in the United States or Canada.

• A PhD in a related field is preferred but not mandatory.

• Experience with advanced language models, intermediate representations, static analysis, SMT/SAT solving, theorem proving, constrained generation, domain-specific languages, healthcare interoperability standards, or safety-critical systems is an added advantage.

🏝️ Benefits

• Comprehensive health, dental, and vision insurance.

• Flexible paid time off (PTO).

• Paid parental leave.

• Remote work opportunities with multiple offsite events throughout the year.