Staff ML Engineer

Gainbridge; Zionsville (IN); On-site; USD 190,000 - 215,000; Full time

Benefits offered by this job

Comprehensive health, dental, and vision insurance plans

401(k) plan with company matching contributions

Job summary

Gainbridge is seeking a Staff ML Engineer to lead MLOps initiatives and contribute to the development of AI/ML products. Your expertise will help shape the ML platform and ensure reliable ML workloads run efficiently on our stack.

This role involves defining integrations, establishing CI/CD pipelines, and mentoring other engineers. Candidates should have extensive experience in ML engineering, with solid technical skills in AWS, model serving, and CI/CD practices.

Additionally, you will benefit from comprehensive health insurance and a competitive salary structure ranging from $190,000 to $215,000 annually.

Qualifications

• 6–10 years in ML engineering, MLOps or platform engineering.
• Experience building ML infrastructure that others build upon.
• Ability to work cross‑functionally with data scientists and engineers.

Responsibilities

• Define ML workloads integration with our ecosystem.
• Establish CI/CD pipelines for ML.
• Mentor senior ML engineers and technical leads.

Skills

MLOps & Model Serving

CI/CD for ML

AWS & Cloud Infrastructure

Monitoring & Observability

Core ML Fundamentals

Feature Engineering Infrastructure

Experiment Tracking & Registry

Education

Bachelor’s degree in Computer Science, Data Science, Engineering or related field

Tools

SageMaker

Terraform

Kubeflow

MLflow

Job description

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.