ML Engineer

We are seeking a skilled ML Engineer with deep expertise in developing, optimizing, and deploying machine learning models, with a focus on healthcare applications.

This role will center on creating AI models to analyze clinical data, including structured and unstructured medical records, and integrating human-in-the-loop feedback to enhance model performance.

You will work hands-on with large language models (LLMs) and other machine learning techniques, collaborating closely with data engineers, software engineers, and clinical stakeholders to integrate AI solutions into scalable system architectures.

The ideal candidate will have experience in healthcare data environments and a strong understanding of the challenges associated with clinical datasets.

About the Role Required Qualifications & Experience Must be located within 50 miles of client headquarters in Woodlawn, MD.

Required to work onsite in Woodlawn 5 days per week. 5+ years of experience in machine learning development with a focus on healthcare or related domains, using frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Experience developing and optimizing machine learning models for tasks such as predictive modeling, anomaly detection, and classification.

Proficiency in processing structured and unstructured data, using techniques such as feature engineering, data preprocessing, and embeddings (e.g., TF-IDF, Word2Vec, BERT).

Experience maintaining high-throughput ML models in production environments.

Expertise in managing dev / test / prod deployment pipelines, with a strong understanding of structuring pipelines for efficient model training and inference.

Proficiency with cloud platforms (AWS, Azure), containerization (Docker), and CI / CD pipelines for machine learning model deployment.

Advanced expertise in Python, with experience in libraries such as Transformers, Scikit-learn, Pandas, NumPy, and PyTorch.

Requires proficiency in query languages such as SQL (PostgreSQL) and NoSQL (MongoDB).

Strong knowledge of programming concepts and tools, including regular expressions and version control (Git).

Experience building and fine-tuning LLMs, with an understanding of challenges such as model scalability, compute resource constraints, and bias mitigation.

Experience deploying machine learning models, including Generative AI, in production environments.

Familiarity with hyperparameter tuning and model evaluation in production pipelines.

Understanding of ethical AI principles and their application in sensitive domains like healthcare.

Master’s degree in Data Science, Machine Learning, Computer Science, or a related field with 10+ years of experience, or a PhD with 4+ years of experience.

Strong ability to articulate technical challenges and solutions effectively.

Excellent written and verbal communication skills.

Knowledge of Agile development methodologies.

Preferred Skills Ideally, the above experience is in a clinical or healthcare context.

The ideal candidate will excel if they have experience working with healthcare data (e.g., electronic health records, lab results, physician notes, imaging reports), familiarity with healthcare data standards and formats (e.g., HL7, FHIR, ICD codes, SNOMED), and exposure to clinical workflows or medical terminologies.

Experience integrating human-in-the-loop feedback into ML systems, leveraging tools like SciPy or NumPy to improve model accuracy and reliability.

Familiarity with advanced machine learning techniques, including deep learning, reinforcement learning, and transformers applied to healthcare data.

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