Machine Learning Engineer
ExaCare AI
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New York, NY
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Mid-Senior level
Posted March 29, 2026
Job link
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Responsibilities
Commitments
Responsibilities
- Novel Solution Development: Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems.
- Rapid Prototyping & Iteration: Build and manage efficient pipelines for rapid experimentation and hypothesis testing.
- Experiment Tracking: Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations, using tools like MLflow or Weights & Biases.
- Model Deployment: Deploy models into production environments using CI/CD practices and model serving frameworks.
- Performance Monitoring: Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability.
- Advanced Model Optimization: Apply modern techniques to optimize models for inference speed, memory footprint, and cost.
- This includes quantization, pruning, and knowledge distillation
- Data Lifecycle Management: Lead efforts in dataset creation, augmentation, and curation to build high-quality, robust training data.
- Advanced Architectures: Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs) What You'll Bring
Commitments
Flexible PTO, take what you need
If we slow down, someone else will pass us.
Not Met Priorities
What still needs stronger evidence
Requirements
- Advanced Architectures: Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs) What You'll Bring
- 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.
Preferred Skills
- Familiarity with advanced model architectures like Transformers and Mixtures of Experts (MoE).
- Contributions to open-source ML projects or a portfolio of personal projects demonstrating a passion for the field.
- Strong, hands-on understanding of the MLOps lifecycle and associated tools (e.g., Docker, Kubernetes, MLflow, Kubeflow, Prometheus).
Education
- (Not required) – Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.
- (Not required) – Contributions to open-source ML projects or a portfolio of personal projects demonstrating a passion for the field.
Company Overview ExaCare AI is a leading health tech company on a mission to build the AI operating system for post-acute care. Our platform turns messy, unstructured referral packets into clear clinical insights and next steps, so teams can make faster, safer placement decisions with less administrative burden. Today, ExaCare AI powers more than 1,500 facilities, and is growing rapidly. We recently raised a $30M Series A led by Insight Partners, and are bringing world-class talent together to transform healthcare. If you like building, learning, and want to make a real impact, come join us! About the Role We are seeking a highly adaptable, creative, and well-rounded Machine Learning Engineer to join our team. You will own the end-to-end ML lifecycle, from dataset creation and foundational research to building and deploying production-grade models. If you thrive in an environment where you can quickly iterate, experiment with cutting-edge techniques, and see your work make a tangible impact, this is the role for you. What You'll Do
Novel Solution Development: Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems.
Rapid Prototyping & Iteration: Build and manage efficient pipelines for rapid experimentation and hypothesis testing.
Experiment Tracking: Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations, using tools like MLflow or Weights & Biases.
Model Deployment: Deploy models into production environments using CI/CD practices and model serving frameworks.
Performance Monitoring: Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability.
Advanced Model Optimization: Apply modern techniques to optimize models for inference speed, memory footprint, and cost. This includes quantization, pruning, and knowledge distillation
Data Lifecycle Management: Lead efforts in dataset creation, augmentation, and curation to build high-quality, robust training data.
Advanced Architectures: Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs) What You'll Bring
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. Bonus Points (Preferred Qualifications):
Familiarity with advanced model architectures like Transformers and Mixtures of Experts (MoE).
Contributions to open-source ML projects or a portfolio of personal projects demonstrating a passion for the field.
Strong, hands-on understanding of the MLOps lifecycle and associated tools (e.g., Docker, Kubernetes, MLflow, Kubeflow, Prometheus). Benefits And Perks
Competitive salary and equity in a high-growth startup
Flexible PTO, take what you need
Medical, dental, and vision coverage
Great startup culture, including ChatGPT premium and company off-sites
High-achieving team, including ex-Amazon engineers and alumni of Bain, BCG, and Goldman Sachs An insight into our Core Values Only the Best Belong Here We are unapologetic about talent. This should be the best team you have ever been on. Protecting that standard is how we honor each other’s time, ambition, and craft. The Patient is Downstream of Every Decision At the end of the day, this is about the patient. We get there by deeply respecting post-acute operators and partners, because their work is the path our software travels to create better care. Raise the Bar on Ownership We grow because people here go beyond the minimum. We invest extra effort, care, and ownership into what we build. The World is Moving Fast. We move Faster. This is a race. We work hard, we move early, and we stay ahead of problems and competitors. If we slow down, someone else will pass us. Radical Candor, Zero Politics. We say what’s true, early, and we keep communication direct and clean so the team can move. Bring Good Vibes and Win Together We win as a team. We bring energy, support each other, and make the workplace somewhere people are excited to show up to. If this sounds like you, we'd love to have a chat!
Novel Solution Development: Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems.
Rapid Prototyping & Iteration: Build and manage efficient pipelines for rapid experimentation and hypothesis testing.
Experiment Tracking: Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations, using tools like MLflow or Weights & Biases.
Model Deployment: Deploy models into production environments using CI/CD practices and model serving frameworks.
Performance Monitoring: Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability.
Advanced Model Optimization: Apply modern techniques to optimize models for inference speed, memory footprint, and cost. This includes quantization, pruning, and knowledge distillation
Data Lifecycle Management: Lead efforts in dataset creation, augmentation, and curation to build high-quality, robust training data.
Advanced Architectures: Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs) What You'll Bring
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. Bonus Points (Preferred Qualifications):
Familiarity with advanced model architectures like Transformers and Mixtures of Experts (MoE).
Contributions to open-source ML projects or a portfolio of personal projects demonstrating a passion for the field.
Strong, hands-on understanding of the MLOps lifecycle and associated tools (e.g., Docker, Kubernetes, MLflow, Kubeflow, Prometheus). Benefits And Perks
Competitive salary and equity in a high-growth startup
Flexible PTO, take what you need
Medical, dental, and vision coverage
Great startup culture, including ChatGPT premium and company off-sites
High-achieving team, including ex-Amazon engineers and alumni of Bain, BCG, and Goldman Sachs An insight into our Core Values Only the Best Belong Here We are unapologetic about talent. This should be the best team you have ever been on. Protecting that standard is how we honor each other’s time, ambition, and craft. The Patient is Downstream of Every Decision At the end of the day, this is about the patient. We get there by deeply respecting post-acute operators and partners, because their work is the path our software travels to create better care. Raise the Bar on Ownership We grow because people here go beyond the minimum. We invest extra effort, care, and ownership into what we build. The World is Moving Fast. We move Faster. This is a race. We work hard, we move early, and we stay ahead of problems and competitors. If we slow down, someone else will pass us. Radical Candor, Zero Politics. We say what’s true, early, and we keep communication direct and clean so the team can move. Bring Good Vibes and Win Together We win as a team. We bring energy, support each other, and make the workplace somewhere people are excited to show up to. If this sounds like you, we'd love to have a chat!