ML Ops Engineer
Posted 22 Apr 2026
Advertised as “Principal ML Ops Engineer ”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- People management
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
Location: Cambridge, MA (Eastern Time / UTC -4) Relocation package available or Remote option for Out-Of-State applicants
Start date: ASAP
Languages: English (required)
About the Role
Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers.
We are seeking a Staff / Principal ML Ops Engineer to lead the design, implementation, and scaling of the companys ML infrastructure and production AI systems. This is a high-impact, architecture-defining role where youll work across the entire model lifecycletraining, evaluation, deployment, observability, and continuous optimization.
You will partner closely with AI researchers, GPU systems engineers, backend teams, and product stakeholders to ensure the companys large-scale AI systems are robust, efficient, automated, and production-grade. This role is ideal for someone who has already built and owned ML platforms at scale and can drive strategy as well as hands-on execution.
What Youll Do
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Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring.
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Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters.
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Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows).
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Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost).
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Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models.
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Collaborate with researchers to productionize models and accelerate training/inference pipelines.
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Establish ML Ops best practices, internal standards, and cross-team tooling.
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Mentor engineers and influence architectural direction across the entire AI platform.
What Are Looking For
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Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level expected).
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Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).