MLOps Engineer
Posted 16 Jul 2026
Advertised as “Senior MLOps Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
No salary listed for this role.
Field of studyComputer Science (or related)
Study levelBachelor's
Field of studyComputer Science (or related)
Study levelMaster's
Palo Alto, CA | Full-Time | On-site
About Us:
At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and product company, we equip enterprises with a comprehensive AI stack to build customized, secure intelligence tailored to their unique business needs.
Driven by advanced Small Language Models and our dynamic metamodel framework, our flagship platforms - Nace Data Intelligence and the Nace SLM Cloud - enable true end-to-end business process automation. The result is transformative ROI: professional services firms using Nace AI are currently recovering 1,000 hours per client engagement, drastically reducing overhead and accelerating delivery.
The work we are doing has a meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company’s trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth.
If building a world-class AI team from the ground up excites you, we’d love to talk.
Role Overview:
As a Senior MLOps Engineer, you will own the infrastructure that takes Nace.AI's models from research to reliable, production-grade systems. Our infrastructure generates task-specific Small Language Models (SLMs) in real time — which means our training, serving, and evaluation infrastructure isn't an afterthought; it is the product. You will design and operate the pipelines, orchestration, and serving layers that allow us to train, deploy, monitor, and continuously improve many specialized models at once, with the reliability that high-stakes audit, compliance, and finance workflows demand. This role sits at the intersection of ML engineering, LLM inference infrastructure, and platform reliability, and requires both strong systems instincts and hands-on execution.
Key Responsibilities:
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Design, build, and operate end-to-end ML infrastructure: training orchestration, experiment tracking, model registries, CI/CD for models, and automated evaluation pipelines.
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Own LLM/SLM serving infrastructure — scale low-latency, high-throughput inference using frameworks like vLLM, including batching, caching, and autoscaling strategies.
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Build and manage multi-GPU training and inference clusters (scheduling, utilization, cost optimization) across cloud and on-prem environments.