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, deep expertise and a can‑do culture. 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. How You’ll Contribute 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, 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. What 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 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. Nice to Have Palantir Foundry, Kubernetes, Bedrock, cost optimization strategies for ML workloads. Education Bachelor’s degree in Computer Science, Data Science, Engineering or related field. Master’s degree or equivalent experience preferred. Experience 6–10 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. Preferred Qualifications 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. Competencies and Soft Skills Executive presence with ability to influence senior leadership and drive organizational change. Strategic vision with ability to define long‑term technical direction aligned with business goals. Strong leadership skills with proven ability to develop and mentor senior technical talent. Exceptional communication skills with ability to articulate technical strategy to executive audiences. Political acumen with ability to navigate complex organizational dynamics and build consensus. Compensation The base pay for this position ranges from $190,000 per year in our lowest geographic market up to $215,000 per year in our highest geographic market. Pay is based on factors such as market location, job‑related skills and experience. Benefits Highlights Comprehensive health, dental, and vision insurance plans for employees and families. Basic and supplemental life insurance; short and long‑term disability coverage. Immediate access to the Employee Assistance Program and wellness programs. 401(k) plan with company matching contributions. #J-18808-Ljbffr