Senior Machine Learning Engineer

Proximie

Develop and deploy machine learning solutions improving clinical outcomes at Proximie. Utilizing multi-modal data to drive productivity and enhance intelligent systems in operating rooms.

Posted 7/4/2026full-timeBeirut • 🇱🇧 LebanonSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing, training, and deploying machine learning models, particularly in healthcare applications. Proficient in utilizing deep learning frameworks and MLOps practices to ensure robust model performance and lifecycle management.

Highest-signal resume keywords

PhD In Machine LearningMachine Learning Model DevelopmentDeep Learning Frameworks (TensorFlow/PyTorch)MLOps PracticesPython Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningMulti-Modal ModelsData CurationModel ImplementationModel TrainingModel ValidationModel DeploymentModel MaintenanceGenerative AI MethodsSupervised Learning

Soft Skills

Clear Communication

Tools & Technologies

Proximie EnvironmentAWS

Industry Keywords

Healthcare ApplicationsHeterogeneous Data DistributionsDeep LearningSelf-Supervised LearningUnsupervised Learning

Tech Stack

Tools & technologies

AWSPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Collaborate with product, engineering and commercial teams to develop and deploy AI models for real world application in hospitals all over the world.
Design, train and validate machine learning mono and multi-modal models using state of the art approaches.
Develop models and derived tools robust to the heterogeneity of operating room environments.
Own the full model lifecycle including but not limited to data curation, model implementation, training, validation, deployment, and maintenance.
Development within Proximie environment to enable dynamic model training and performance evaluation while integrating with Proximie’s data lakes.
Document solutions and contribute to internal knowledge sharing and capability building.

Requirements

What you’ll need

PhD in a machine learning field such as computer science, data science, engineering, or a related field. Masters considered but PhD preferred.
Minimum of 4 years’ hands-on experience in industry, developing and deploying AI solutions which solve real-world problems.
Expertise in developing, training and fine-tuning machine learning and multi-modal models.
Experience in training models with data originating from heterogeneous distributions is highly desirable.
Deep knowledge of a variety of traditional machine learning, deep learning and generative AI methods for both supervised, self-supervised and unsupervised learning with an emphasis on vision.
Proficiency with deep learning frameworks such as TensorFlow/PyTorch.
Proficiency with Python and strong software development background.
Knowledge and experience with AWS is highly desirable.
Experience with MLOps practices, including versioning, deployment, and monitoring of models highly desirable.
Ability to communicate complex technical concepts clearly to non-technical stakeholders.

Benefits

Comp & perks

Generous annual leave.
Two “well-being” days per year plus the day off for your birthday.
“Summer Fridays” – early office closing on Fridays during summer months.
Annual bonus programme – based on individual contribution.
Access to an annual stipend of $1,000 to assist with personal development activities.
Flexible working hours.
A flat organizational structure where every opinion matters, ideas are cultivated, and innovation is encouraged.
Opportunities to see the world in a truly global company with teams across the UK, Europe, United States and the Middle East.