Staff ML Engineer

Group 1001 • Zionsville, IN

10d

About The Position

Group 1001 is a consumer-centric, technology-driven family of insurance companies on a mission to deliver outstanding value and operational performance by combining financial strength and stability with deep insurance expertise and a can-do culture. Group1001’s culture emphasizes the importance of collaboration, communication, core business focus, risk management, and striving for outcomes. This goal extends to how we hire and onboard our most valuable assets – our employees. Why This Role Matters: We're building AI&ML-powered products that will transform how Group 1001 approaches pricing optimization, claims automation, and risk intelligence. To do this at scale, we need robust ML infrastructure—not just great models. As a Staff ML Engineer, you'll focus on the MLOps and infrastructure layer that makes ML production-ready: model serving, feature pipelines, experiment tracking, and CI/CD for ML. You'll help shape our ML platform architecture, working alongside Platform Engineering teams to ensure ML workloads run reliably on our modern stack: Snowflake, Dagster, Coalesce, Palantir and AWS SageMaker. This role is for engineers who are as passionate about infrastructure, deployment, and operationalizing ML as they are about the models themselves Please note, this position requires an in-person interview.

Requirements

• MLOps & Model Serving : Hands-on experience with model serving frameworks (SageMaker Endpoints, Seldon Core, BentoML, Ray Serve, or TensorFlow Serving); building and operating inference infrastructure at scale
• CI/CD for ML : Building ML pipelines with SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and deployment automation
• AWS & Cloud Infrastructure : Strong AWS experience—SageMaker, EKS/ECS, Lambda, Step Functions, S3, IAM; infrastructure-as-code (Terraform, CDK, CloudFormation)
• Monitoring & Observability : Model monitoring, drift detection, alerting; tools like Evidently, WhyLabs, SageMaker Model Monitor, or custom solutions
• Core ML Fundamentals : Working knowledge of Python, ML frameworks (PyTorch, TensorFlow, scikit-learn), and model evaluation—enough to partner effectively with data scientists
• Feature Engineering Infrastructure: Experience with feature stores (SageMaker Feature Store, Feast, Tecton, or similar); designing feature pipelines for both batch and real-time serving
• Experiment Tracking & Registry: MLflow, Weights & Biases, SageMaker Experiments, or similar; establishing reproducibility and governance across ML projects
• Bachelor's degree in Computer Science, Data Science, Engineering, or related field
• 7-10 years in ML engineering, MLOps, or platform engineering with a focus on productionizing ML systems
• Demonstrated experience building ML infrastructure that others build upon—serving layers, feature stores, or MLOps tooling
• Track record of improving ML delivery velocity through infrastructure and automation
• Proven ability to work cross-functionally with data scientists, platform engineers, and stakeholders
• Experience mentoring and developing senior engineers and technical leaders
• Strong executive presence with ability to influence stakeholders at all levels of the organization

Nice To Haves

• Palantir Foundry, Kubernetes, Bedrock, cost optimization strategies for ML workloads
• Master's degree or equivalent experience preferred
• Experience in insurance or financial services with deep understanding of industry challenges
• Recognized expertise through conference presentations, publications, or industry speaking engagements
• Experience with enterprise-scale systems and complex technical environments
• Proven ability to build consensus and drive alignment across multiple teams and stakeholders

Responsibilities

• Partner with Data & Platform Engineering to define how ML workloads integrate with our Snowflake-Dagster-Palantir ecosystem
• Evaluate and recommend tooling for the ML stack—balancing build vs. buy decisions against our scale and compliance needs
• Contribute to platform roadmap discussions, advocating for infrastructure investments that accelerate ML delivery
• Establish CI/CD pipelines for ML: automated testing, model validation, staged deployments, and rollback capabilities using SageMaker Pipelines, Step Functions, or similar orchestration
• Implement model monitoring and observability: drift detection, performance degradation alerts, and automated retraining triggers
• Architect ML workloads on AWS: SageMaker (Training Jobs, Processing, Endpoints), EC2/EKS for custom serving, S3 for artifact storage, and IAM for secure access patterns
• Optimize for cost and performance—right-sizing instances, spot instance strategies, auto-scaling endpoints, and efficient GPU utilization
• Integrate ML infrastructure with our Dagster orchestration layer for end-to-end pipeline visibility
• Mentor senior ML engineers and technical leads, developing the next generation of ML engineering leadership

Benefits

• Employees who meet benefit eligibility guidelines and work 30 hours or more weekly, have the ability to enroll in Group 1001’s benefits package.
• Employees (and their families) are eligible to participate in the Company’s comprehensive health, dental, and vision insurance plan options.
• Employees are also eligible for Basic and Supplemental Life Insurance, Short and Long-Term Disability.
• All employees (regardless of hours worked) have immediate access to the Company’s Employee Assistance Program and wellness programs—no enrollment is required.
• Employees may also participate in the Company’s 401K plan, with matching contributions by the Company.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level