Senior ML Engineer: Scale Production ML Pipelines

6sense; Northern (KY); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health coverage

Paid parental leave

Stock options

Generous PTO

Job summary

6sense is seeking a Sr. Machine Learning Engineer to join the ML Engineering team. The role focuses on production systems that ensure dependable ML at massive scale, including model-training pipelines, evaluation and release controls, and high-throughput inference.

You will work with Data Scientists, platform engineers, and product teams to turn experimentation into governed, observable, scalable production systems and help shape evaluation, promotion, monitoring, and reliability.

Qualifications

• 6+ years of industry experience building and operating production machine-learning or data-intensive distributed systems.
• Strong Python engineering skills and practical experience designing maintainable services and pipelines.
• Demonstrated MLOps depth: experiment tracking, model registry/versioning, CI/CD, reproducible training, deployment strategies, monitoring.

Responsibilities

• Own production ML capabilities end to end: turn a business need into a pipeline or service, then operate and improve it in production.
• Build and evolve training, model-refresh, feature/data-validation, and inference workflows across batch and real-time use cases.
• Design model lifecycle controls: experiment tracking, evaluation gates, rollout, rollback, lineage, reproducibility, and monitoring.
• Build platform primitives that enable data science and AI product teams to ship faster while maintaining reliability and cost.
• Improve performance and observability of distributed ML workloads and model-serving systems.
• Collaborate with Data Science on evaluation design, data quality, model-health signals, and production debugging.

Skills

Python

MLOps

Distributed systems

Data pipelines

Tools

Spark

Ray

Databricks

Kubernetes

AWS

Job description

6sense is seeking a Sr. Machine Learning Engineer to join the ML Engineering team. The role focuses on production systems that ensure dependable ML at massive scale, including model-training pipelines, evaluation and release controls, and high-throughput inference.

You will work with Data Scientists, platform engineers, and product teams to turn experimentation into governed, observable, scalable production systems and help shape evaluation, promotion, monitoring, and reliability.