Lead MLOps Platform Engineer – Distributed Training & Kubeflow

Opportunity Overview

Location: 100% Remote (United States)
Employment Type: Full-Time Direct-Hire via Accel DNA
Compensation Range: $180,000 – $220,000 Base + Equity
Hiring Agency: Accel DNA – Premier Frontier Data & AI Talent Partner

Construct internal developer platforms for automated model experimentation, distributed fine-tuning, and governed artifact tracking across AI research squads.

Key Responsibilities:

• Architect and maintain scalable MLOps platforms using Kubeflow, MLflow, and cloud-native Kubernetes operators.
• Implement standardized CI/CD pipelines automating data validation, distributed model training, and automated canary deployment.
• Manage feature stores, model registry governance, and lineage tracking for audit compliance.
• Reduce model iteration cycle time from weeks to hours by providing self-service GPU experiment environments.

What You Bring:

• 6+ years of software/platform engineering with 3+ years dedicated to MLOps platforms.
• Expertise in Kubernetes architecture, Argo Workflows, Docker, and infrastructure-as-code (Terraform).
• Familiarity with PyTorch distributed training launchers (Torchrun) and cloud storage architectures.
• Strong focus on developer experience (DevEx) and internal tooling ergonomics.

How to Apply:

Interested in exploring this opportunity? Submit your profile or portfolio directly to contact@accel-dna.com or apply through https://accel-dna.com/contact-us/.