Senior Machine Learning Engineer

Senior ML Engineer

About Our Client

Our client is revolutionizing healthcare by streamlining knowledge workflows with AI. They reduce administrative burdens and elevate outcomes, empowering professionals to focus on what truly matters: improving lives. They’re growing fast and looking for bold, driven teammates to join them.

**This role is open to candidates who have existing US work authorization. Sponsorship not available at this time**

The Opportunity

Our client is seeking a Senior Machine Learning Engineer to lead the development and deployment of machine learning solutions with an emphasis on large language models (LLMs), vision models, and classic NLP (Natural Language Processing) models. The ideal candidate will have a proven track record in these areas, particularly within healthcare contexts, and will play a significant role in advancing their AI-driven, healthcare-optimized AI Copilots and Agents.

Key Responsibilities

• Fine-tune or prompt engineer large language models (LLMs) for various healthcare applications across diverse customer engagements
• Develop and refine approaches for handling vision-based data using state-of-the-art VLM models capable of accurately processing and analyzing medical documents, healthcare forms, and other visual data
• Create and enhance classic NLP models to support clinical documentation, patient interaction, and other healthcare needs
• Collaborate with multi-disciplinary teams including data scientists, ML engineers, healthcare clients, and product managers to deliver robust solutions
• Ensure models are efficiently deployed and integrated into healthcare systems, maintaining high performance and scalability
• Mentor and guide junior engineers and data scientists, fostering a culture of learning and innovation
• Conduct rigorous testing, validation, and tuning of models to ensure accuracy, reliability, and compliance with healthcare standards
• Apply training techniques including distributed training on GPUs and TPUs
• Stay informed on the latest research, tools, and technologies in ML, especially those applicable to language and vision processing in healthcare
• Document methodologies, model architectures, and project outcomes for both technical and non-technical audiences

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field
• 5–7 years of experience in machine learning engineering, with a strong track record of developing production-grade models and pipelines in regulated industries such as healthcare
• Hands-on expertise with large language models (e.g., GPT, BERT), computer vision models, and classic NLP technologies
• Proficiency in Python and experience with ML libraries/frameworks such as TensorFlow, PyTorch, OpenCV, etc.
• Strong understanding of deep learning techniques, fine-tuning, hyperparameter optimization, and model optimization
• Proven experience deploying and managing ML models in production environments
• Excellent analytical and problem-solving skills with a strategic mindset
• Strong communication skills for articulating complex concepts to diverse audiences
• Working knowledge of MLOps and LLMOps tools (e.g., MLflow, Kubeflow)
• Knowledge of software engineering best practices and classic ML techniques/frameworks
• Nice to have: Experience with cloud-based ML platforms (Azure ML, SageMaker)

Who You Are

• Owner mentality — you take full responsibility, learn what’s needed, and get it done
• Naturally curious, experimental, and eager to push boundaries
• Passionate, committed to the team and mission, and eager to learn and succeed together
• Strong communicator across multiple mediums, able to work effectively in a remote/global team

Nice-to-Have Competencies

• Experience deploying NLP/ML in production for large or complex organizations
• Experience efficiently scaling ML model training and inference
• Familiarity with big data technologies such as Kafka, Spark, Hadoop, Snowflake

What Our Client Offers

• Opportunity to make a real impact on the future of healthcare
• Autonomy, ownership, and the ability to chart your own growth path
• Competitive compensation and benefits
• 100% employer-paid health, vision, and dental insurance
• Retirement plans (401k), disability insurance, and employee assistance programs