AI & ML Engineering
Browse roles for engineers building production ML systems, model pipelines, and applied AI products.
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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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50 data scientists. Every one deploys differently. Some use a shared EC2 instance. Some maintain their own containers. There is no model registry that...
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Six ML models in production. All deployed via cron jobs. No monitoring. Retraining is a manual process involving two engineers and a lot of optimism....
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Posted 5 months ago
Our ML team is scaling fast and our infra is not keeping up. We've got 12 researchers queuing jobs on a cluster that's half on-prem, half GCP, all hel...
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We're moving our ML infrastructure from a setup that was fine when we had three data scientists to one that can support ten and scale to twenty. Right...
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Role summary: ML Platform Engineer, mid-level. Company: B2B SaaS, 85 employees, Series A. Team: Platform Engineering (4 engineers + 1 manager). Report...
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Posted 5 months ago
We're training large transformer models (7B–30B parameters) and our training setup is inefficient. Single-machine multi-GPU only. No FSDP, no ZeRO opt...
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Posted 5 months ago
We're standardising our ML platform on Google Cloud's Vertex AI and need an ML engineer with deep Vertex AI and GCP experience to lead the migration f...
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Posted 5 months ago
We run a large Hadoop cluster that's expensive, slow to maintain, and blocking our data team. We want to migrate our batch processing workloads to Dat...
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Posted 6 months ago
We have 15+ ML models in production and no proper model registry, versioning, or CI/CD. Models are deployed by hand, documentation is scattered, and r...
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Posted 6 months ago
We're migrating from a legacy on-premise data warehouse (PostgreSQL + custom ETL scripts) to Snowflake with dbt for transformations and Airflow for or...
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Explore opportunities built for candidates in LLM systems, machine learning, MLOps, automation, and applied AI delivery.
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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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