1,081 total jobs
178 new jobs this week
Deploy ProcessOS into enterprise production environments, building and debugging workflows, agents, and integrations while guiding customer adoption and feeding implementation learnings back into the product.
Develop and deliver NICE CXone AI solutions, implementing Cognigy, Omilia, and Proactive AI Agent features while owning software development, testing, defect resolution, and production support.
The Internal Audit team is seeking a graduate-level intern with a strong accounting and finance background to support financial and operational audit engagements.
Lead the architecture, engineering, and deployment of enterprise-grade AI solutions, transforming ambiguous business problems into scalable, secure AI systems.
Architect and deliver production AI systems for legal reasoning and document analysis, advancing LLM infrastructure, evaluation, retrieval, and product features while guiding engineers and shaping technical strategy.
Own production reliability and complex incident response across AWS and Databricks, partnering with customers and engineering teams to resolve issues, automate operations, and turn field learnings into lasting platform improvements.
The Auto Tagger team is the engine behind our data flywheel, responsible for translating petabytes of raw, multi-modal vehicle data into a highly curated library of critical driving scenarios.
A Failure Analysis Engineer will interact with multiple internal and external teams, acting as a technical forensic and bridge between functions. Here’s a breakdown of the key collaborators:.
A Failure Analysis Engineer will interact with multiple internal and external teams, acting as a technical forensic and bridge between functions. Here’s a breakdown of the key collaborators:.
A Failure Analysis Engineer will interact with multiple internal and external teams, acting as a technical forensic and bridge between functions. Here’s a breakdown of the key collaborators:.
Build and scale backend services and AI workflows, including reliable search, retrieval, inference, and orchestration systems, while balancing latency, accuracy, user experience, and operational reliability.
Develop and optimize neural network-based tabular ML systems, profiling training and inference pipelines and implementing performance-critical components in Rust to improve efficiency, reliability, and scalability.
Drive innovation in AI model serving and inference architectures, optimizing deployment strategies for efficient and scalable performance across diverse applications.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy across resource-constrained edge devices.
Develop and evaluate compression strategies for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy across resource-constrained edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy and enabling efficient deployment on edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy across resource-constrained edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy across resource-constrained edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy for efficient deployment on edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy for efficient deployment on edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy and enabling efficient deployment on edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy for efficient deployment on edge devices.
Develop and evaluate compression methods for multimodal AI models, reducing model size, memory use, and inference latency while preserving accuracy across resource-constrained edge devices.
Research and engineer vision-language models across the development lifecycle, from multimodal data curation and training to evaluation and optimization for efficient, real-world deployment.
Those that list pay offer a median of $190,000 a year. The middle half pay between $165,000 and $225,000.
| Level | Range | Middle half · median |
|---|---|---|
| Mid-level | $139K–$218K · $184K | |
| Senior | $167K–$222K · $190K | |
| Lead | $194K–$245K · $220K |
30% of these listings post pay. From 125 listings with pay in USD, at most three per company.
5% of these listings are open worldwide and 2% to a whole region, like Europe or Latin America. The other 93% hire in specific countries:
From 927 listings that state where they hire.
What hiring companies here ask for far more often than across all remote jobs, so it is worth putting front and center on your resume.
Share of the 431 companies hiring here that ask for each skill, and how many times as often as across all remote jobs. Soft skills like communication are left out.
Half stop taking applications within 34 days of being posted, and 1 in 4 within 12 days. Applying in the first week counts.
12 days
1 in 4 closed by then
34 days
Half closed by then
76 days
3 in 4 closed by then
From 530 listings that closed in the last 120 days, at most three per company.
