Senior Software Engineer, ML Ops

About Us: 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. We’re backed by First Round Capital, FirstMark Capital, Foundation Capital, Brewer Lane Ventures, and have been named as Inc.’s top places to work! 

The Adventure: We're the leading AI Guidance Platform in the insurance industry today, working with some of the largest insurance carriers in the US and expanding globally. We are growing incredibly fast and have a nearly 100% success record in converting pilots to production deployments. We read batch daily data from SQL tables and present them to the user in awesomely intuitive products, which are built on a shared, unified platform. Our engineering culture values simplicity, core engineering principles, quality, honesty, transparency and strong collaboration. In this role you'll have the opportunity to help build key workflows in the system such as rules based engines that will span across all of our products.

Your Impact: In this role, you will play a critical part in ensuring that our growing client base is consistently supported by the latest and most robust ML infrastructure. As we process increasing volumes of data, you will be responsible for scaling our systems to meet these demands, ensuring seamless and efficient model development, deployment, and maintenance across all clients. You will collaborate with our platform and infrastructure teams to shape the future of EvolutionIQ’s ML Platform. From technology choices to building the roadmap, you will have a large amount of ownership and scope in this role. As a senior member of the team, you will also mentor and guide other engineers, fostering a culture of continuous learning and helping them develop new skills in MLOps and related technologies, while driving technical excellence across the board.

Role Responsibilities:

• Design, develop, and automate scalable infrastructure and pipelines for training, validation, and deployment of machine learning models, while also managing large-scale data processing and ensuring smooth integration with CI/CD workflows
• Implement comprehensive monitoring and alerting systems to track the performance of models and data pipelines, detect issues such as drift and data quality problems, and manage model lifecycle tasks like retraining and versioning
• Optimize model serving and data processing infrastructure for high availability, low latency, and cost-efficiency
• Collaborate with the ML Platform team and other cross-functional teams to align infrastructure and data pipelines with the latest company standards and client requirements
• Explore opportunities to engage in feature engineering and other machine learning development activities to enhance model performance, with the option to contribute more directly to these areas if you have an interest
• Contribute to a culture of inclusivity and collaboration by actively participating in team discussions, providing constructive feedback, and supporting the professional growth of colleagues through mentorship and knowledge sharing

Skill Requirements:

• Strong expertise in developing, deploying, and maintaining scalable machine learning pipelines and infrastructure in production environments, ideally using Google Cloud Platform (GCP) services
• Proficiency in Python for backend development, with experience in ML libraries such as TensorFlow, PyTorch, and Scikit-learn
• Experience in building and maintaining CI/CD pipelines and orchestration tools (e.g., Airflow, Kubeflow, Dagster) for machine learning and data processing workflows, or similar tools
• Strong skills in containerization (Docker, Kubernetes) and model serving frameworks (e.g., TensorFlow Serving, MLflow) for optimizing performance and scalability
• Strong problem-solving skills, with experience in monitoring, troubleshooting, and improving the performance of ML models and data pipelines, with a focus on security, compliance, and data privacy
• Excellent communication and collaboration skills, with the ability to work effectively in diverse, cross-functional teams
• A commitment to continuous learning and development, with an openness to explore new tools, techniques, and approaches in the rapidly evolving field of MLOps

Work-life, Culture & Perks: 

• Compensation: The salary range for this role is $190-225K with flexibility,  plus meaningful equity plan
• Well-Being: Full medical, dental, vision, short- & long-term disability, 401k matching. 100% of the employee contribution up to 3% and 50% of the next 2%
• Home & Family: Flexible PTO, 100% paid parental leave (4 months for primary caregivers and 3 months for secondary caregivers), sick days, paid time off. For new parents returning to work we offer a flexible schedule. We also offer sleep training to help you and your family navigate life schedules with a newborn
• We also have a flexible vacation policy and are closed for winter break at the end of the year 
• Office Life: Catered lunches, happy hours, and pet-friendly office space. $500 for your in home office setup and $200/year for upgrades every year after your initial setup
• Growth & Training: $1,000/year for each employee for professional development, as well as upskilling opportunities internally
• Sponsorship: We are open to sponsoring candidates currently in the U.S. who need to transfer their active H1-B visa

EvolutionIQ appreciates your interest in our company as a place of employment. EvolutionIQ is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees