Company Overview

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 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)

Qualifications

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!