Machine Learning Engineer (Model Dev)

jobgether; New York (NY); Remote; USD 140,000 - 180,000; Full time

Benefits offered by this job

Remote work environment

401(k) with employer matching

Unlimited PTO

Equity as part of compensation

Job summary

jobgether is seeking a Machine Learning Engineer (Model Dev) based in the United States to develop ML solutions with impact on cancer care and clinical decision-making.

You will work across the full model-development lifecycle, from research to production deployment, focusing on multimodal pathology data, including whole-slide images and molecular information, collaborating with clinicians and engineers.

Qualifications

• At least 1 year of experience developing ML/deep-learning models using PyTorch or TensorFlow, including master's research.
• Familiarity with oncology biomarkers and cancer biology, endpoints and risk stratification.
• Experience with real-world datasets and appropriate metrics for evaluation.
• Strong Python skills and modern software practices (version control, testing, maintenance).
• Ability to analyze results, troubleshoot, and communicate conclusions effectively.
• Strong collaboration skills with cross-functional teams of engineers, clinicians and researchers.
• Experience with complex clinical datasets and ML in regulated healthcare environments is valuable.
• Familiarity with self-supervised learning, foundation models, or large-scale model development is a plus.
• Understanding dataset shift across sites, devices, scanners, or protocols is a plus.
• Research output through publications or conferences is valued, as is cloud-based ML development experience.

Responsibilities

• Develop and evaluate AI-based biomarkers from multimodal datasets to predict outcomes and molecular characteristics.
• Contribute to self-supervised foundation models and downstream ML systems (MIL, survival, segmentation, classification).
• Improve model robustness and reproducibility across scanners, institutions and protocols.
• Investigate interpretability techniques to increase clinician trust and model improvement opportunities.
• Build pipelines enabling efficient experimentation, validation, and deployment of ML models.
• Conduct rigorous evaluation, analyze results, troubleshoot behavior, and communicate findings clearly.
• Collaborate with ML scientists, engineers, clinicians, biostatisticians, product, regulatory, and QA teams.
• Support regulatory and quality documentation for AI model development and validation.
• Contribute to publications, conferences, and academia/industry collaborations.

Skills

Python

PyTorch

TensorFlow

Machine learning

Deep learning

Experimentation

Code testing

Version control

Tools

Git

Docker

Kubernetes

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer (Model Dev) based in United States.This role offers the opportunity to develop machine-learning solutions with direct impact on cancer care and clinical decision-making. You will work across the full model-development lifecycle, from research and experimentation through validation and production deployment. The position focuses on multimodal clinical and pathology data, including whole-slide images, molecular information, and longitudinal patient data. You will collaborate closely with ML scientists, engineers, clinicians, biostatisticians, product teams, and regulatory partners. A key focus will be improving model robustness, interpretability, reproducibility, and performance across diverse clinical environments. You will also contribute to foundation-model development, scientific research, publications, and the advancement of medical AI. This is an opportunity to combine strong engineering skills with meaningful scientific and healthcare impact.

Accountabilities

• Develop and evaluate AI-based biomarkers using multimodal datasets, including whole-slide images, clinical variables, and molecular data, to predict patient outcomes, treatment benefit, and molecular characteristics.
• Contribute to self-supervised foundation models and downstream machine-learning systems, including multiple-instance learning, survival and hazard models, segmentation, and classification.
• Design and evaluate approaches that improve model robustness and reproducibility across scanners, institutions, staining protocols, and patient populations.
• Investigate model interpretability techniques to explain predictions, strengthen clinician trust, and identify opportunities for model improvement.
• Build and enhance tools, workflows, and pipelines that enable efficient, reproducible experimentation, validation, and deployment of machine-learning models.
• Conduct rigorous model evaluation, analyze experimental results, troubleshoot model behavior, and communicate technical findings clearly.
• Collaborate with ML scientists and engineers alongside product, biostatistics, clinical development, regulatory, and quality teams throughout model development and validation.
• Support regulatory and quality documentation associated with AI model development, evaluation, and validation.
• Contribute to peer-reviewed research, conference presentations, and collaborations with academic and industry partners.

Requirements

• At least 1 year of experience developing machine-learning or deep-learning models using PyTorch, TensorFlow, or comparable frameworks, including relevant master's-level or graduate research experience.
• Familiarity with oncology and biomarker development, including cancer biology, treatment pathways, clinical endpoints, risk stratification, and the characteristics of clinically actionable biomarkers.
• Experience working with real-world datasets and applying appropriate metrics and validation methodologies to evaluate machine-learning models.
• Strong Python programming skills and familiarity with modern software-engineering practices, including version control, testing, code review, and maintainable development workflows.
• Ability to analyze experimental results, investigate model behavior, troubleshoot technical issues, and communicate conclusions effectively.
• Strong collaboration skills and the ability to work effectively with machine-learning engineers, scientists, clinicians, and other cross-functional stakeholders.
• Experience with complex clinical datasets such as medical imaging, multi-omics, longitudinal patient records, clinical studies, or multi-institutional cohorts is highly valuable.
• Familiarity with weakly supervised learning, multiple-instance learning, survival analysis, or related machine-learning techniques is preferred.
• Experience with self-supervised representation learning, foundation models, or large-scale model development is advantageous.
• Understanding of dataset shift and variability across sites, devices, scanners, staining methods, or acquisition protocols is a plus.
• Exposure to machine learning in regulated healthcare environments, including SaMD, FDA 510(k) or De Novo pathways, design controls, or CLIA/LDT validation, is desirable.
• Research experience demonstrated through publications, conference presentations, internships, or academic projects is valued.
• Familiarity with cloud-based ML development, distributed training, workflow orchestration, experiment tracking, or reproducible ML pipelines is a plus.

Benefits

• Base salary of $140,000–$180,000 per year, depending on experience, qualifications, and other relevant factors.
• Equity as a core component of the overall compensation package.
• 401(k) plan with employer matching.
• Unlimited paid time off (PTO).
• Remote work environment.
• Opportunity to work on challenging medical-AI problems with potential to improve cancer diagnosis, treatment decisions, and patient outcomes.
• Collaboration with experienced ML scientists and engineers as well as clinical, biostatistics, product, and regulatory professionals.
• Opportunities to contribute to publications, conference presentations, and external academic or industry collaborations.
• Inclusive and equal-opportunity workplace committed to bringing together diverse perspectives and backgrounds.