Senior/Staff Machine Learning Engineer (Tech Lead, Model Dev)
Artera, an AI company building foundation models for cancer care, seeks a senior/staff ML engineer to serve as tech lead for AI biomarker development — owning the full lifecycle from problem framing through model validation, regulatory submission, and production deployment. Core work involves deep learning in PyTorch/TensorFlow on multimodal clinical data (whole-slide pathology images, clinical va
- Compensation
- $180K–$260K / yr
- Location
- United States
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 5+ years of industry experience building deep learning systems in PyTorch (or TensorFlow)
- 2+ years as technical lead for complex ML programs, including launching and monitoring ML products in production
- Depth in oncology and biomarker development: cancer biology, treatment pathways, clinical endpoints, risk stratification, clinically actionable biomarkers
- Demonstrated ability to lead complex technical programs: scoping, technical direction, sequencing, managing dependencies and risk on dated deliverables
- Proven ability to communicate complex ML concepts to cross-functional, non-ML collaborators
- Experience mentoring or managing ML scientists and engineers
Nice to have
- Experience building ML on complex clinical data (medical imaging, multi-omics, longitudinal records), including weakly supervised learning and handling site/device/protocol variation
- Experience developing ML in a regulated environment: FDA 510(k)/De Novo, CE/UKCA, SaMD, design controls, or CLIA/LDT validation
- Experience with self-supervised representation learning (e.g., DINOv3) and adapting medical foundation models to downstream clinical tasks
- Experience with data from randomized controlled trials and multi-institutional clinical cohorts
- Peer-reviewed publications, conference presentations, and external academic/industry collaborations
- Experience with cloud-scale training and workflow orchestration (Flyte/Union, Kubernetes, AWS), experiment tracking, and reproducible ML pipelines
Full description
About Us: Artera is an artificial intelligence company dedicated to transforming cancer care. We’ve developed foundation models that analyze clinical and pathology data, generating actionable insights that guide therapy selection and improve outcomes for cancer patients. By continuously improving these models, we aim to uncover the biological mechanisms driving cancer progression.
We're looking for an experienced machine learning engineer and technical lead to own AI biomarker development end to end — from problem framing with clinical and biostatistics partners, through model development and validation, to regulatory submission and production deployment. You'll provide technical direction for complex, cross-functional programs, mentor other ML scientists and engineers, and drive decisions across the model-development lifecycle. Beyond owning a biomarker program, you'll take on the hardest cross-cutting problems in our field: robustness across scanners and sites, mechanistic interpretability of model decisions, and the next generation of our pathology foundation models.
Essential Responsibilities:
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Serve as the technical lead for patient-facing AI products, defining the technical strategy and driving execution in partnership with product, biostatistics, clinical development, and regulatory/quality.
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Design and build AI-based biomarkers on multimodal data — including whole-slide images, clinical variables, and molecular data — to predict patient outcomes, treatment benefit, and molecular traits.
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Advance our core self-supervised foundation models and the downstream architectures built on them (multiple-instance learning, time-to-event / hazard models, segmentation and classification components), with generalization as a first-order objective.
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Own score reproducibility across scanners, institutions, staining protocols, and patient populations.
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Develop and integrate mechanistic interpretability methods to explain model decisions, build clinician trust, and drive actionable model improvements.
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Architect tools and processes that streamline the end-to-end model development lifecycle — from prototyping through production deployment and monitoring — ensuring efficiency, reproducibility, regulatory compliance, and scale.
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Author and defend regulatory and quality documentation, and represent AI in design and development reviews.
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Lead technical planning and delivery: break multi-quarter programs into milestones, manage dependencies across AI, platform, biostatistics, and clinical teams, surface risks early, and hold submission and launch dates.