Top Hybrid Machine Learning Engineer Jobs

As a ML Engineer at Apiphany, you will develop advanced machine learning models, focusing on engineering data, AI systems, and real-world performance.

The role involves developing machine learning models for robotics in medical imaging, collaborating with engineers and clinicians to prototype and deploy systems for autonomous surgical interventions.

The Senior Architect will lead client projects focusing on AI/ML solutions, including model design, implementation, and deployment across various platforms, while mentoring junior team members.

Own clinician-facing products that surface AI-gathered patient context, support decision-making, and reduce documentation burden. Partner with engineering, ML, clinical, and design teams to build explainability, review/override workflows, and measurable workload reductions. Validate workflows with practicing physicians and lead safety and escalation protocols with partners.

The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

The role involves developing advanced machine learning algorithms, solving complex optimization problems using reinforcement learning techniques, and leading technical projects while optimizing solutions for operational efficiency.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

The Machine Learning Scientist will build and integrate ML models, analyze data, design scalable pipelines, and ensure robust model testing.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.