ML Engineer

Role Overview

We are seeking a talented Machine Learning Engineer who will work closely with data scientists, product managers, and engineers to implement machine learning solutions that solve real-world problems in industries like healthcare. You will focus on designing and building scalable, efficient machine learning systems, improving model performance, and driving innovation within RISA Labs’ AI-driven platform.

This is an exciting opportunity to apply your skills in a high-impact, mission-driven environment and help shape the future of AI-powered infrastructure.

Responsibilities

• Model Development: Design, implement, and optimize machine learning models, including supervised/unsupervised learning, deep learning, and reinforcement learning.
• Model Deployment: Work on the end-to-end lifecycle of machine learning models — from training and validation to deployment, monitoring, and continuous improvement in production environments.
• Data Analysis & Feature Engineering: Analyze large datasets, clean and preprocess data, and identify key features to improve model accuracy and performance.
• Performance Optimization: Continuously improve and optimize machine learning models for speed, scalability, and accuracy, focusing on real-time applications in high-volume environments.
• Collaboration: Collaborate with cross-functional teams to integrate machine learning models into RISA’s platform, ensuring that they meet business needs and user requirements.
• Research & Innovation: Stay up-to-date with the latest research and industry trends in machine learning and AI, bringing new ideas and techniques to improve our systems.
• Infrastructure Development: Work on building scalable infrastructure for machine learning workflows, including data pipelines, model management, and performance tracking.
• AI Ethics & Compliance: Ensure that machine learning models are ethically sound and comply with industry regulations, such as HIPAA, when applied to sensitive data in regulated industries.

Qualifications

• 3+ years of experience in machine learning engineering, data science, or related fields
• Proficiency in Python and machine learning frameworks like TensorFlow, PyTorch, or scikit-learn
• Solid experience with machine learning algorithms, model optimization, and deep learning techniques
• Experience with cloud platforms (AWS, GCP, or Azure) and deploying machine learning models in production
• Strong understanding of data structures, algorithms, and computational complexity
• Familiarity with distributed computing tools (e.g., Spark, Hadoop) and data pipelines
• Experience with model versioning, experiment tracking, and building machine learning workflows (e.g., MLflow, Kubeflow)
• Familiarity with data privacy and security standards (e.g., HIPAA) when working with sensitive data
• Excellent communication skills and the ability to work collaboratively in a cross-functional team
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field, or equivalent experience
• Bonus: Experience working in healthcare, life sciences, or other regulated industries