Senior/Staff Machine Learning Engineer – Model Dev

Artera.net

. Lead the technical effort and define the strategic vision for patient-facing products with product, biostatistics, clinical development, and regulatory/quality teams .

Posted 9/18/2026full-timeRemote • United StatesSenior💰 $180,000 - $240,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI-based biomarkers and deep learning systems, particularly in oncology and clinical data environments. Proven ability to lead technical teams, manage complex projects, and communicate effectively with cross-functional stakeholders.

Highest-signal resume keywords

Deep Learning Systems DevelopmentTechnical Leadership in Machine LearningOncology and Biomarker DevelopmentRegulated Environment ExperienceProject Management and Risk Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PyTorchTensorFlowSelf-Supervised LearningMachine Learning on Clinical DataModel Development LifecycleMechanistic Interpretability MethodsRandomized Controlled Trial Data AnalysisMulti-Omics Data IntegrationScore ReproducibilityClinical Endpoints and Risk Stratification

Soft Skills

Effective CommunicationMentoring and CoachingCollaboration with Cross-Functional Teams

Tools & Technologies

Cloud-Scale TrainingWorkflow OrchestrationExperiment TrackingReproducible ML Pipelines

Industry Keywords

FDA 510(k)De NovoCE/UKCASaMDCLIA/LDT ValidationCancer BiologyClinical DevelopmentBiostatisticsPatient OutcomesMulti-Institutional Clinical Cohorts

Tech Stack

Tools & technologies

CloudPyTorchTensorflow

About the role

Key responsibilities & impact

Lead the technical effort and define the strategic vision for patient-facing products with product, biostatistics, clinical development, and regulatory/quality teams
Design and build AI-based biomarkers using whole-slide images, clinical variables, and molecular data to predict patient outcomes, treatment benefit, and molecular traits
Advance self-supervised foundation models and downstream architectures, including multiple-instance learning, time-to-event/hazard models, segmentation, and classification
Own score reproducibility across scanners, institutions, staining protocols, and patient populations
Develop and integrate mechanistic interpretability methods to explain model decisions and improve models
Architect tools and processes for the end-to-end model development lifecycle from prototyping through production deployment and monitoring
Author and defend regulatory and quality documentation and represent AI in design and development reviews
Plan and manage multi-quarter delivery milestones, dependencies, risks, submission dates, and launch dates
Publish in peer-reviewed journals and present at clinical and ML venues; support external collaborations
Mentor and coach machine-learning scientists and engineers and raise standards for scientific rigor, code quality, and written communication

Requirements

What you’ll need

5+ years of industry experience building deep learning systems in PyTorch (or TensorFlow)
2+ years of experience as a technical lead, launching and monitoring machine-learning products in production environments
Demonstrated depth in oncology and biomarker development, including familiarity with cancer biology and treatment pathways, clinical endpoints, risk stratification, and clinically actionable biomarkers
Demonstrated project management ability, including scoping, sequencing, and managing dependencies and risk across multiple teams on dated deliverables
Proven ability to communicate complex ML concepts effectively to cross-functional, non-ML collaborators
Experience mentoring or managing ML scientists and engineers
Experience building ML on complex clinical data, including medical imaging, multi-omics, or longitudinal patient records, weakly supervised learning, and variation across sites, devices, and protocols
Experience developing ML in a regulated environment, such as FDA 510(k)/De Novo, CE/UKCA, SaMD, design controls, or CLIA/LDT validation
Experience with self-supervised representation learning and adapting medical foundation models to downstream clinical tasks
Experience with randomized controlled trial data and multi-institutional clinical cohorts
Peer-reviewed publications and conference presentations, with external academic or industry collaborations
Experience with cloud-scale training and workflow orchestration, experiment tracking, and reproducible ML pipelines

Benefits

Comp & perks

Equity
401k matching
Unlimited paid time off (PTO)