Staff ML Engineer

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 themselvesPlease note, this position requires an in-person interview.How You'll Contribute:Partner with Data & Platform Engineering to define how ML workloads integrate with our Snowflake-Dagster-Palantir ecosystemEvaluate and recommend tooling for the ML stack—balancing build vs. buy decisions against our scale and compliance needsContribute to platform roadmap discussions, advocating for infrastructure investments that accelerate ML deliveryEstablish CI/CD pipelines for ML: automated testing, model validation, staged deployments, and rollback capabilities using SageMaker Pipelines, Step Functions, or similar orchestrationImplement model monitoring and observability: drift detection, performance degradation alerts, and automated retraining triggersArchitect ML workloads on AWS: SageMaker (Training Jobs, Processing, Endpoints), EC2/EKS for custom serving, S3 for artifact storage, and IAM for secure access patternsOptimize for cost and performance—right-sizing instances, spot instance strategies, auto-scaling endpoints, and efficient GPU utilizationIntegrate ML infrastructure with our Dagster orchestration layer for end-to-end pipeline visibilityMentor senior ML engineers and technical leads, developing the next generation of ML engineering leadershipWhat We're Looking For:Technical Skills: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 scaleCI/CD for ML: Building ML pipelines with SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and deployment automationAWS & 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 solutionsCore ML Fundamentals: Working knowledge of Python, ML frameworks (PyTorch, TensorFlow, scikit-learn), and model evaluation—enough to partner effectively with data scientistsFeature Engineering Infrastructure: Experience with feature stores (SageMaker Feature Store, Feast, Tecton, or similar); designing feature pipelines for both batch and real-time servingExperiment Tracking & Registry: MLflow, Weights & Biases, SageMaker Experiments, or similar; establishing reproducibility and governance across ML projectsNice to Have: Palantir Foundry, Kubernetes, Bedrock, cost optimization strategies for ML workloadsEducation:Bachelor's degree in Computer Science, Data Science, Engineering, or related fieldMaster's degree or equivalent experience preferredExperience:7-10 years in ML engineering, MLOps, or platform engineering with a focus on productionizing ML systemsDemonstrated experience building ML infrastructure that others build upon—serving layers, feature stores, or MLOps toolingTrack record of improving ML delivery velocity through infrastructure and automationProven ability to work cross-functionally with data scientists, platform engineers, and stakeholdersExperience mentoring and developing senior engineers and technical leadersStrong executive presence with ability to influence stakeholders at all levels of the organizationPreferred Qualifications:Experience in insurance or financial services with deep understanding of industry challengesRecognized expertise through conference presentations, publications, or industry speaking engagementsExperience with enterprise-scale systems and complex technical environmentsProven ability to build consensus and drive alignment across multiple teams and stakeholdersCompetencies and Soft Skills:Executive presence with ability to influence senior leadership and drive organizational changeStrategic vision with ability to define long-term technical direction aligned with business goalsStrong leadership skills with proven ability to develop and mentor senior technical talentExceptional communication skills with ability to articulate technical strategy to executive audiencesPolitical acumen with ability to navigate complex organizational dynamics and build consensusBenefits Highlights: 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.Group 1001, and its affiliated companies, is strongly committed to providing a supportive work environment where employee differences are valued. Diversity is an essential ingredient in making Group 1001 a welcoming place to work and is fundamental in building a high-performance team. Diversity embodies all the differences that make us unique individuals. All employees share the responsibility for maintaining a workplace culture of dignity, respect, understanding and appreciation of individual and group differences.