TELUS Digital is building a global community of experts to support the development and evaluation of advanced Artificial Intelligence systems.
Remote Technology (AI) Jobs in United States 2026
433 remote jobs open to candidates in United States in Technology (AI). Updated daily.
micro1 is selecting Software Engineers to support a customer’s innovative project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
Project Overview We are sourcing experienced technical specialists in CVE and Application Security to audit tasks used to train and evaluate AI systems.
<< Insert Job Description Here>> Project Overview We are sourcing experienced technical specialists in Machine Learning to audit tasks used to train and evaluate AI systems.
Project Overview We are sourcing experienced technical specialists in AWS Serverless and Infrastructure as Code to audit tasks used to train and evaluate AI systems.
roject Overview We are sourcing experienced technical specialists in AI-Assisted Developer Workflows to audit tasks used to train and evaluate AI systems.
Project Overview We are sourcing experienced technical specialists in GPU Kernels to audit tasks used to train and evaluate AI systems.
Project Overview We are sourcing experienced technical specialists in AWS Trainium and Neuron Kernel Interface (NKI) to audit tasks used to train and evaluate AI systems.
Project Overview We are sourcing experienced technical specialists in Software Engineering (SWE-Bench) to audit tasks used to train and evaluate AI systems.
We are sorcing experienced technical specialists in Kubernetes to audit tasks used to train and evaluate AI systems. The objective of this project is to ensure that AI training workflows and tasks are technically rigorous, practical, and highly accurate within your specific area…
Evaluate the quality and correctness of AI-assisted software-development traces used to train and evaluate a frontier AI lab's models.
Evaluate the quality, correctness, and reproducibility of software-engineering benchmark tasks used to train and evaluate a frontier AI lab's models.
Evaluate the quality, correctness, and methodological rigor of applied machine-learning tasks used to train and evaluate a frontier AI lab's models.
Evaluate the quality, correctness, and cloud-architecture soundness of AWS serverless and infrastructure-as-code tasks used to train and evaluate a frontier AI lab's models.
Evaluate the quality, correctness, and production-readiness of Kubernetes tasks used to train and evaluate a frontier AI lab's models.
Analytics engineers who live in dbt, BigQuery, and Snowflake to build the SQL scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
BI and data visualization experts in Tableau, Power BI, and Looker to build the dashboard scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
Cloud engineers to build the infrastructure and automation scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
Network engineers to build the routing, switching, and troubleshooting scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
Security analysts to build the detection and incident response scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models.
Evaluate the quality, correctness, and hardware-appropriateness of Neuron Kernel Interface (NKI) development tasks used to train and evaluate a frontier AI lab's models.
Evaluate the quality, correctness, and completeness of GPU/accelerator kernel development tasks used to train and evaluate a frontier AI lab's models.
Evaluate the quality, fidelity, and completeness of vulnerability-reproduction and remediation tasks used to train and evaluate a frontier AI lab's models.
Open to candidates in Washington D.C., New York, Washington, California and Massachusetts only. Can you bring together globally distributed teams and the systems they build into a production-quality reinforcement learning ecosystem for researchers and model builders?