Summary
✨ AI‑Generated
A software engineering role focused on building AI-powered data platforms and scalable backend systems. The position involves developing intelligent features, reliable processing pipelines, and user-facing solutions while maintaining high standards for security and data quality.
Highlights
Work on advanced AI and data infrastructure challenges, building reliable systems for sensitive information processing in a remote-friendly environment.
Description
Software Engineer, AI & Data Platform (Python/Go)Vancouver, BC · Remote (Canada)
Why this role is interestingMost engineers building with LLMs right now are building chat interfaces.
You'd be building something harder.
OneMedNet supplies regulatory-grade Real World Data and imaging Real World Data to life sciences, pharma, and medical device researchers.
That means clinical records and medical imaging studies that have to be de-identified, curated, and validated to a standard that holds up under FDA scrutiny — at scale, across a growing network of hospital and clinic partners.
The interesting problems live in that gap: how do you use language models to accelerate curation of messy, high-stakes clinical data without ever compromising accuracy or patient privacy? How do you build inference pipelines that are auditable enough for a regulated environment? How do you make complex cohort data legible to a researcher in a browser?
That's the work.
What you'll doBuild LLM-powered features for clinical data curation and retrieval — prompt design, evaluation, API integration, and the pipelines that hold it all togetherDesign and ship backend services in Python and/or Go that move structured records, unstructured clinical notes, DICOM imaging, and HL7/FHIR messaging securely and fastAnswer questions that close deals.
When Sales is scoping a contract, someone has to query partner systems and determine what data actually exists and in what volume.
That's engineering work, and it directly determines whether a deal happensBuild the interfaces researchers actually use to search, filter, and visualize cohortsOwn quality end to end: instrument your LLM features, monitor for drift and regression, and know when a model output is good enough to shipWork directly with the clinical and data teams who define what "regulatory grade" means for a given dataset
What we need from you2+ years shipping production software with strong Python and/or GoReal experience building on LLM APIs (Anthropic, OpenAI, or similar) — you've shipped something that used one, and you've dealt with the failure modesComfort across the stack.
You don't need to be a design specialist, but you can build a functional, clear interface without waiting on someone elseStrong SQL and database fundamentals — schema design, query performance, and knowing when relational is the wrong answerWorking comfort in AWS and Linux.
Our platform runs on AWS (S3, EC2, RDS, VPC, IAM) in Ubuntu environments.
You should be able to operate there without hand-holdingJudgment about correctness.
In healthcare data, a plausible-but-wrong answer is worse than no answer.
We need engineers who feel that in their bones
Our stackPython and Go.
AWS (S3, EC2, RDS, VPC, IAM) on Ubuntu Linux.
Snowflake and Palantir for data and AI workloads.
Anthropic and OpenAI APIs.
Postgres.
Git-based workflows.
Nice to haveHealthcare, clinical data, or medical imaging experience (DICOM, HL7/FHIR)Working knowledge of HIPAA, GDPR, or technical de-identification techniques for PHI/PIISnowflake or Palantir experienceExperience with model evaluation frameworks or LLM observability toolingData visualization workIf you hit most of the core list and none of these, apply anyway.
We'll teach you the healthcare part.
Compensation & benefits$80,000 – $120,000 CAD depending on experience, plus performance bonusMedical, dental, and vision insuranceUnlimited PTOFlexible schedule and fully remote across CanadaA small team at a real inflection point in an emerging market
OneMedNet is committed to privacy and compliance, meeting or exceeding HIPAA, GDPR, and other applicable privacy standards to protect patient information.