Staff ML Engineer

Group 1001

Staff ML Engineer focusing on MLOps and infrastructure for ML production at Group 1001. Collaborating with Data & Platform Engineering to ensure ML workloads run reliably.

Posted 6/3/2026full-timeZionsville • 🇺🇸 United StatesLead💰 $190,000 - $215,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowAWSEC2RayTensorflow

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

Bachelor's degree in Computer Science, Data Science, Engineering, or related field
Master's degree or equivalent experience preferred
6-10 years in ML engineering, MLOps, or platform engineering with a focus on productionizing ML systems
Hands-on experience with model serving frameworks (SageMaker Endpoints, Seldon Core, BentoML, Ray Serve, or TensorFlow Serving)
Experience building ML pipelines with SageMaker Pipelines, Kubeflow, Airflow, or Dagster
Strong AWS experience—SageMaker, EKS/ECS, Lambda, Step Functions, S3, IAM (infrastructure-as-code)
Experience with feature stores (SageMaker Feature Store, Feast, Tecton, or similar)
Experience with experiment tracking tools (MLflow, Weights & Biases, SageMaker Experiments, or similar)
Experience mentoring and developing senior engineers and technical leaders
Proven ability to work cross-functionally with data scientists, platform engineers, and stakeholders.

Benefits

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

ML engineeringMLOpsmodel serving frameworksSageMaker PipelinesKubeflowAirflowAWSfeature storesexperiment tracking toolsCI/CD pipelines

Soft Skills

mentoringcross-functional collaborationadvocacyleadership

Certifications

Bachelor's degreeMaster's degree