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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Posted 4 months ago
United States — Los Angeles (UTC-7)
Smartwatches collect heart rate, SpO2, activity, and sleep data continuously — but most of that data sits unused until someone has a health scare and...
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Most companies running AI in production have no idea what it actually costs. GPU instances spin up and never get terminated. Models serve traffic at 5...
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Our clients' models work. They're just too expensive to run. A single inference call costs $0.12 when it should cost $0.003. Latency is 800ms when the...
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Thirteen models in production. Four different deployment patterns. Zero standardization. That's where we are today, and that's exactly the problem you...
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We build the APIs that connect AI models to business applications. Not the models themselves — the plumbing around them. Authentication, rate limiting...
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We already have the data. Three years of clickstream events, purchase histories, and customer support interactions from our e-commerce platform — all...
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Every AI feature we ship needs to be tested, but traditional QA doesn't work when the output is non-deterministic. You can't write a unit test that sa...
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GPU clusters are the most expensive line item in any AI company's budget, and most teams have no idea what next month's bill will look like. A single...
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Posted 4 months ago
United States — Los Angeles (UTC-7)
Rolling back a model version sounds simple until you realize the downstream impact. A recommendation model rollback might invalidate cached recommenda...
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Static autoscaling rules waste GPU capacity. Scale up too aggressively and you pay for idle GPUs during traffic dips. Scale up too slowly and requests...
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Explore opportunities built for candidates in LLM systems, machine learning, MLOps, automation, and applied AI delivery.
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Start with the specialty that matches your strengths, then review roles designed for practical AI work, stronger fit, and faster decision-making.
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.
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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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