AI & ML Engineering
Browse roles for engineers building production ML systems, model pipelines, and applied AI products.
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Catastrophe loss estimation is the actuarial foundation of reinsurance pricing, portfolio accumulation management, and regulatory capital adequacy. Wh...
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Mutation testing is the gold standard for measuring test suite quality: introduce small synthetic bugs (mutations) into the source code and measure wh...
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Posted 3 months ago
United States — Chicago (UTC-5)
Deploying a new model version seems like it should be safe once the model passes accuracy tests. But a model that changed its output schema — returnin...
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Posted 3 months ago
United States — Los Angeles (UTC-7)
Training a large model for 14 days and losing everything to a hardware fault on day 13 is not a theoretical scenario — it happens regularly on GPU clu...
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API documentation lives or dies on the quality of its code samples. A code sample that doesn't run, imports a deprecated package, uses an API that was...
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Transaction fraud that bypasses per-transaction models is usually graph fraud: a ring of accounts connected through shared devices, addresses, or refe...
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Posted 3 months ago
United States — Chicago (UTC-5)
An ML team running 40 experiments needs to answer one question: which model should we deploy? Answering it requires comparing performance across held-...
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Posted 3 months ago
United States — Los Angeles (UTC-7)
When a data pipeline fails, the data for that time window doesn't just disappear — it needs to be recovered and processed to fill the gap. When a sche...
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End-to-end tests are valuable and expensive to write. The expensive part isn't the assertion — it's figuring out the precise sequence of UI interactio...
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Context windows are getting longer. 128K, 200K, 1M tokens. The naive conclusion is that chunking becomes less important — just put everything in conte...
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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.
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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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