We are seeking experienced ML platform engineers and MLOps engineers (or backend engineers focused on model serving infrastructure) to provide expert insight as part of a project for our AI Marketplace. No preparation required — just your real-world experience.

This is a one-time, paid, project-based interview engaged on a contract basis— not a full-time role.

Location: U.S.-based experts only

Engagement: One-time, project-based interview

Work Type: Remote

Estimated rate: $135/hour (prorated for the 30-minute task)

What You’ll Do

You’ll complete a ~30-minute recorded AI-led video interview where you explain how you actually do your work in practice.

This interview will focus on:

• Packaging models and deploying them to batch or real-time serving systems
• Managing model registries, versioning, rollout strategies, rollback, and serving parity
• Monitoring live model performance, latency, drift, fairness drift, and incident signals
• Running retraining-to-redeploy loops and production incident response

You will be asked to explain workflows specifically within: deployment, serving, monitoring, drift detection, incident response, rollback, and retraining workflows.

You should only apply if you can confidently walk through these areas step-by-step based on your own experience and respond to AI-led interview questions with depth, clarity, and real-world examples.

What We’re Looking For

• 6+ years in MLOps, ML platform engineering, model serving, or production machine learning infrastructure
• Direct, hands-on experience in deployment, serving, monitoring, drift detection, incident response, rollback, and retraining workflows
• Experience making or influencing decisions related to deployment readiness, rollout strategy, rollback triggers, drift thresholds, retraining approvals, and production incident containment
• Familiarity with tools such as: Kubernetes, Docker, Airflow, Argo, MLflow, model registry tools, Feast, Prometheus, Grafana, Datadog, SageMaker, Vertex AI
• Strong ability to clearly explain workflows, decisions, and reasoning
• Experience owning or directly executing these workflows in a real production environment
• Experience working cross-functionally with HR, legal, payroll, finance, HRIS, business leaders, people managers, or compensation committee stakeholders as relevant to the workflow

About the Interview

The interview is designed to elicit step-by-step explanations of real workflows and decision-making processes, with an emphasis on detailed, experience-based responses rather than high-level summaries.

This is a project-based engagement. Selected participants will be paid for completing a recorded AI-led video interview; hourly rates are estimates based upon anticipated time of completion. Participants will control their own schedule, provide their own tools to complete the interview, and may participate in other opportunities as they choose.

Once you complete the interview, we’ll send your video recording and transcript, along with AI-generated insights, to the internal research team and AI lab partner. This data will be used to generate a structured list of tasks associated with your profession and for AI model improvement.

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