Deep learning shows up in the posting text of these roles. It is extracted from prose the employer wrote, not from a structured field, so treat it as a strong hint about the work rather than a stated requirement — the row badges show the requirements that are stated (clearance, work authorization, customer site, travel).
Extracted automatically from public job postings. Always verify details on the original posting before applying.
Klaviyo Palo Alto, CA Applied AI Onsite
Train and deploy large-scale Machine Learning models in production systems to improve product value for customers.
PyTorchTensorFlowSparkHugging FaceAgentsMachine LearningDeep LearningRecommender Systems
Scale AI San Francisco, CA; Seattle, WA; New York, NY Applied AI Onsite
Analyze model behavior, design and build benchmarks, and diagnose failure modes in large language models.
FineTuningAgentsdeep learningreinforcement learninglarge-scale model fine-tuningRLHFreward modelingLLM evaluation
Sigma Computing New York City, NY Applied AI Onsite
Builds and deploys production-grade AI/ML systems, partnering with teams to identify opportunities and develop infrastructure.
PythonSQLAWSAzureGCPDatabricksSparkAgents
Sigma Computing New York City, NY Applied AI Onsite
Builds AI/ML systems, prototypes, and infrastructure for a data analytics platform.
PythonSQLAWSAzureGCPDatabricksSparkAgents
Scale AI San Francisco, CA; Seattle, WA; New York, NY Applied AI Onsite
Develop novel post-training techniques for large-scale generative models, analyze model behavior, and publish research findings.
FineTuningEvalsDeep learningReinforcement learningLarge-scale model fine-tuningRLHFPreference modelingInstruction tuning
Scale AI Doha, Qatar; London, UK FDE Onsite
Build and deploy end-to-end AI applications into production, collaborating with public sector clients and cross-functional teams.
PythonTypeScriptJavaScriptJavaAWSAzureGCPFineTuning
Remote
By country
By city
By skill
Hard requirements to check first — clearance, work authorization, visa sponsorship, travel and customer-site requirements are extracted from the posting text, so you can filter them out before spending time on an application.