Machine Learning Engineer

ExaCare AI

United States

On-site

USD 170,000 - 720,000

Full time

14 days+

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Job summary

An innovative health tech company in the United States seeks a Machine Learning Engineer to manage the end-to-end ML lifecycle. The candidate should have proven experience in deploying models and a solid background in Python and deep learning frameworks. This full-time position offers competitive compensation, with a salary range potentially reaching $720,000. If you thrive in a fast-paced environment and are passionate about AI, we want to hear from you.

Qualifications

3+ years of experience in building and deploying machine learning models.
Expert-level proficiency in Python programming.
Experience with systematic hyperparameter searching.
Direct experience with Large Language Models (LLMs).
Exceptional skills in reproducible research.

Responsibilities

Research and implement novel machine learning solutions.
Build and manage efficient pipelines for rapid experimentation.
Deploy models into production environments.
Implement and maintain monitoring systems for model performance.

Skills

Python

Deep learning frameworks (e.g., PyTorch)

Hyperparameter optimization

Dataset creation and curation

Model optimization techniques (e.g., quantization)

Education

Bachelor's or Master's degree in Computer Science, AI, or Data Science

Tools

MLflow

Docker

Kubernetes

Job description

We are a trailblazing health tech company on a mission to revolutionize the nursing home & post acute space. Our innovative AI software is transforming the admissions process and care delivery in these settings. We’ve just raised $30M and are experiencing rapid growth. We are looking for a Machine Learning Engineer to join our growing team.

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.

Key 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 hypothesisn
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)

Must-Have Qualifications

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).

If this sounds like you, we'd love to have a chat!

Seniority level