Machine Learning Engineer

About RISA Cancer patients don’t just fight the disease - they fight the system. Today, life-saving treatments are routinely delayed by days or even weeks due to manual, error-prone workflows. At RISA Labs, we’re here to fix that. RISA’s platform - Business Operating System as a Service (BOSS) - is not another automation bot or AI assistant. It’s a full-stack orchestration engine built for the vertical complexity of healthcare. Instead of relying on humans to push paperwork or brittle bots that break when systems change, BOSS decomposes complex workflows into micro-tasks, then delegates them to a network of intelligent agents: LLMs, digital twins, and reinforcement learners, extending across an institution’s entire software stack. This allows BOSS to create a parallel digital workforce, operating on behalf of teams and alongside them. A 1,000-person institution can function like a 2,000-person one overnight, with digital agents making up half the workforce. 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 Show more Show less