Machine Learning Engineer

🕒 March 18

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ExaCare AI

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

🔥 Funding within the last year

💰 $30M Series A - ExaCare on 2025-10

Artificial Intelligence • SaaS • B2B

ExaCare AI is an AI-powered SaaS platform purpose-built for post-acute care providers (skilled nursing facilities and home health agencies) that automates referral review, insurance verification, and reimbursement workflows to speed admissions, protect revenue, and improve patient fit. The system condenses clinical records into concise overviews, flags clinical and financial risks (including PDPM opportunities), checks coverage and eligibility, and provides portfolio-level visibility into occupancy, census trends, and operational metrics to help admissions teams respond faster and make more confident decisions.

📋 Description

• Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems. • Build and manage efficient pipelines for rapid experimentation and hypothesis testing. • Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations. • Deploy models into production environments using CI/CD practices and model serving frameworks. • Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability. • Apply modern techniques to optimize models for inference speed, memory footprint, and cost. • Lead efforts in dataset creation, augmentation, and curation to build high-quality, robust training data. • Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs).

🎯 Requirements

• Proven experience (3+ years) in building, training, and deploying machine learning models in a production environment. • Expert-level proficiency in Python • Experience with modern deep learning frameworks, such as PyTorch. • Demonstrable experience with systematic hyperparameter searching and optimization frameworks (e.g., Optuna, Ray Tune). • Exceptional organizational skills, with a strong emphasis on reproducible research and methodical experiment tracking. • Direct experience with LLMs, including fine-tuning, prompt engineering, RAG, and efficient inference. • Practical experience implementing model optimization techniques like quantization (e.g., bitsandbytes) and pruning. • Experience in designing and curating novel datasets from scratch. • Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.

🏖️ Benefits

• Health insurance • Flexible work arrangements • Professional development opportunities