Job Title: Founding Machine Learning Engineer

Company Name: medical imaging AI organization

Job Url: https://www.linkedin.com/jobs/view/4374247696/?eBP=NOT_ELIGIBLE_FOR_CHARGING&trk=flagship3_search_srp_jobs&refId=V%2BAJtrR%2FWWsKbDR3%2F7P2rA%3D%3D&trackingId=j16SQm9J3IsiUXSUadbrzw%3D%3D

Job Description: Founding Machine Learning Engineer 

United States · 8 hours ago · Over 100 applicants

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Remote

Full-time
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Meet the hiring team


Klaudia Topa 

• 3rd

Principal Global Recruiter within Medical Imaging and AI

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About the job
I am working with a medical imaging AI organisation building infrastructure to address this challenge by enabling rigorous, continuous evaluation of AI systems across their full lifecycle from early development through regulatory submission and post-deployment monitoring. The goal is to help AI developers and healthcare partners better understand model behaviour, improve reliability, and support safer adoption of AI in clinical practice.



They are seeking a Founding Machine Learning Engineer to help define and build the technical and analytical foundations of this approach.



The Role



As a Founding Machine Learning Engineer, you will work directly with medical imaging companies preparing AI systems for FDA 510(k) or De Novo submissions. You will own customer engagements end-to-end, leading technical investigations that generate clear, defensible evidence about how models behave in real-world settings.



The focus of the role is not model development, but understanding and demonstrating model behaviour identifying where models generalise, where they fail, and what uncertainties or trade-offs remain.



You will combine strong machine learning intuition with customer-facing ownership, delivering decision-grade analysis that informs product strategy, deployment decisions, and regulatory readiness.



Key Responsibilities



Lead customer engagements from initial scoping through delivery of evaluation outcomes.
Design and execute investigations assessing model performance across datasets, institutions, and patient subpopulations.
Analyse failure modes, generalisation limits, and performance variability in real-world data.
Identify distribution shift, bias, and uncertainty using statistical and representation-level analysis.
Work with complex and imperfect clinical datasets to produce robust, evidence-backed conclusions.
Translate technical findings into clear insights for both technical and non-technical stakeholders.
Deliver structured evidence supporting internal go/no-go decisions and regulatory submissions.
Contribute to early product direction, evaluation methodology, and technical strategy as a founding team member.


Required Qualifications



Demonstrated experience delivering meaningful machine learning or analytical work (projects, research, or production systems).
Strong empirical ML skills, including experiment design and model debugging.
Ability to investigate model behaviour beyond metrics and identify underlying causes of failure.
Experience reasoning about distribution shift, uncertainty, and real-world data limitations.
High analytical ownership using Python for end-to-end workflows (data analysis → conclusions).
Comfortable working with ambiguity, evolving scope, and messy datasets.
Strong communication skills and confidence presenting technical findings to customers and stakeholders.
… more

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