Machine Learning Engineer

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Position Summary

The Machine Learning Engineer will be responsible for the end-to-end development and deployment of large language and machine learning models, with a primary focus on data preprocessing, model training, and fine‑tuning using large‑scale healthcare datasets. This role requires a strong understanding of large language models, machine learning principles, data engineering, and experience working with sensitive healthcare data.

Key Responsibilities

Data Preprocessing : Clean, transform, and prepare large, complex healthcare datasets for machine learning model development, including handling missing values, outlier detection, feature engineering, and data normalization. Identify, collect, and curate relevant, industry‑specific datasets for model retraining and format data appropriately for the chosen LLM and training pipeline.

Model Training & Fine‑Tuning : Design, train, and fine‑tune various LLMs on extensive healthcare data to solve specific clinical or operational problems. Set up and manage the training environment, including GPU instances and required software; experiment with and fine‑tune hyperparameters such as learning rate, batch size, and training epochs to optimize performance. Integrate structured and unstructured data (multimodal / multi‑input models).

Model Evaluation & Optimization : Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.

Pipeline Development : Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.

Collaboration : Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.

Research & Development : Stay up‑to‑date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies to enhance our solutions.

Documentation : Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.

Requirements

Education : Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.

Experience : 5+ years of Machine Learning Engineering or a similar role.

Proven experience with large‑scale data preprocessing, LLM / model training, and fine‑tuning.

Experience with distributed training (PyTorch Distributed, DeepSpeed, Ray, Hugging Face Accelerate).

Experience with GPU / TPU optimization and memory management for large language models.

Experience working with healthcare data is highly desirable.

Technical Skills

Proficiency in Python and relevant ML libraries (TensorFlow, PyTorch, Scikit‑learn, Pandas, NumPy).

Strong understanding of machine learning algorithms, Large Language Models, and deep learning architectures.

Experience with cloud platforms (GCP, AWS) and distributed computing frameworks (Spark) is a plus.

Familiarity with MLOps practices and tools.

Soft Skills

Excellent problem‑solving and analytical skills.

Strong communication and collaboration abilities.

Ability to work independently and as part of a team in a fast‑paced environment.

Benefits

Competitive salary and benefits package.

Flexible working arrangements (remote or hybrid options available).

The opportunity to work on life‑changing AI technology that directly impacts patient outcomes.

Join a team that combines cutting‑edge innovation with a mission to save lives and improve health equity.

Continuous learning opportunities with access to the latest tools and advancements in AI and healthcare.

Why Join Us?

Joining C the Signs is not just about building AI; it’s about shaping the future of healthcare. If you are a technical leader with an unshakable belief in the power of AI to save lives and the ability to make it happen at scale, this is your opportunity to create a tangible, global impact.

Seniority Level

Mid‑Senior level

Employment Type

Full‑time

Job Function

Engineering and Information Technology; Hospitals and Health Care

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