AI & ML Engineering
Browse roles for engineers building production ML systems, model pipelines, and applied AI products.
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Explore roles for candidates who keep AI systems scalable, observable, reliable, and production-ready.
MLOps and AI infrastructure jobs are ideal for candidates who enjoy deployment, orchestration, observability, platform tooling, and the systems that keep AI reliable at scale.
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Labeling data is expensive. Our clients spend $0.04-$0.12 per labeled example and produce 50,000 new labels per week, which means annotation budgets a...
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Cloud migration cost estimates are notoriously wrong. A Fortune 500 company in our client book budgeted $2.8M for a workload migration and spent $6.1M...
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Posted 4 months ago
United States — Los Angeles (UTC-7)
Our wearable devices generate inference logs, power consumption metrics, and model accuracy signals continuously — but right now that data sits on dev...
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Posted 4 months ago
United States — New York (UTC-4)
Failover for a stateless web service is a solved problem. Failover for an ML serving cluster is not. When a model serving region goes down, you need t...
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Posted 4 months ago
United States — Los Angeles (UTC-7)
LLM inference latency is the core product metric for every API we serve. When a legal research tool waits 8 seconds for a response, lawyers switch tab...
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When Service X degrades, which other services break, and by how much? At most companies, the answer lives in someone's head. At ours, it should be in...
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GPU memory fragmentation is the silent killer of training throughput. A cluster that should deliver 94% MFU sits at 71% because memory allocations fro...
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Posted 4 months ago
United States — Los Angeles (UTC-7)
We manage ML infrastructure for 28 AI companies that don't want to hire dedicated platform teams. Our platform monitors their training clusters, flags...
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Deploying AI models is not like deploying software. A model that passes all unit tests can still regress on edge-case inputs, behave differently acros...
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We process 400 million API calls per day across our platform, and when something goes wrong — a latency spike, an unexpected error rate, a model retur...
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Explore opportunities built for candidates in LLM systems, machine learning, MLOps, automation, and applied AI delivery.
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Browse roles for engineers building production ML systems, model pipelines, and applied AI products.
Browse JobsFind jobs in LLM products, retrieval systems, prompt design, and language-first AI workflows.
Browse JobsExplore opportunities in deployment, monitoring, orchestration, and scalable AI infrastructure.
Browse JobsReview roles for PMs and strategists connecting AI initiatives to roadmap, adoption, and business value.
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Browse JobsBrowse opportunities in analytics, forecasting, experimentation, and decision support for AI teams.
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Some candidates start with AI & ML Engineering jobs for product and model delivery, while others focus on NLP & Prompt Engineering jobs for LLM products, MLOps & AI Infrastructure jobs for production reliability, or AI Product & Strategy jobs for roadmap and adoption work.
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You can explore remote AI jobs across machine learning, prompt engineering, MLOps, data science, automation, and AI product roles.
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