Staff Software Engineer, ML Platform & AI Labs

Position Overview

• Location Type: Remote
• Employment Type: Full-time
• Salary: Not specified

EvolutionIQ’s mission is to improve the lives of injured and disabled workers and enable them to return to the workforce, saving billions of dollars in avoidable costs and lost productivity to the US and global economies and make insurance more affordable for everyone. We are currently experiencing massive growth and to accomplish our goals, we are hiring world-class talent who want to help build and scale internally, and transform the insurance space. Our team is our #1 priority, and we have been named one of Inc.’s Best Workplaces 3 years in a row!

At EvolutionIQ, we are bringing together world-class technical talent who want to invent, solve, and create in an entirely new technology category. For our experts in machine learning, data science, applications, and technology integration, cracking the insurance industry’s previously ‘impossible’ big data problem with deep learning AI is our version of summiting K2 or Everest.

We are seeking a highly skilled and motivated Staff Software Engineer - ML Platform to lead the architecture, deployment, and scaling of our machine learning (ML) and artificial intelligence (AI) infrastructure. This role combines deep technical expertise with strategic impact to drive innovation in the ML pipeline, optimize end-to-end workflows, and ensure robust deployment in production environments. As a Staff ML Platform Engineer, you will collaborate closely with machine learning engineers and senior leadership to streamline experimentation, improve model observability, and enhance overall system performance. You’ll play a pivotal role in setting the MLOps standards across the organization.

Requirements

• Not specified

Responsibilities

• Design, build, and launch scalable ML and data processing systems supporting multi-machine data processing (e.g., MapReduce), GPU/TPU model training, and automated model monitoring systems on cloud platforms.
• Automate model lifecycle management, including training, evaluation, and deployment, to enable fast, safe, and consistent updates across environments.
• Introduce modern, scalable frameworks for model monitoring, feature engineering, hyperparameter tuning, and continuous re-training, ensuring robust model performance over time.
• Lead the deployment of models through REST and gRPC APIs, enabling smooth integration with application frontends and real-time user interaction.
• Continuously research, evaluate, and implement the latest MLOps tools, frameworks, and platforms to improve efficiency, scalability, and reliability of ML operations.
• Implement and manage monitoring systems to track model and data performance, proactively identifying and mitigating issues using tools like Prometheus and Grafana.
• Apply best practices in secure data handling and model integrity within ML workflows, ensuring regulatory and security compliance norms.
• Share MLOps knowledge and improvements in ML engineering workflows through internal training.