NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their...
NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their...
Design and build core platform systems to support technical research on AI-related chemical and biological risks, translating ambiguous research needs into scalable, maintainable software solutions. Work within a multidisciplinary team to develop LLM-based agent systems, evaluation frameworks, and shared infrastructure while mentoring engineers and shaping technical direction. Operate in a high-impact environment with direct influence on AI policy and global governance.
This role involves developing and optimizing machine learning systems for document classification and extraction, with a focus on fraud detection in identity verification. The engineer will work on production-scale LLM and traditional ML pipelines, improving accuracy and performance across global document types. Collaboration with cross-functional teams and mentorship of junior engineers are key aspects of the position.
A Principal-level Software Engineer will lead backend architecture and stability initiatives across a large engineering organization, driving cross-team projects and long-term technical strategy. The role involves solving systemic challenges in a high-scale SaaS environment, working across React and Python monolith systems, and aligning engineering outcomes with commercial goals. Unique exposure to in-house AI research and enterprise-grade video generation technology provides a distinctive technical context.
Develop and maintain a software 'digital twin' to model and benchmark a high-speed knowledge layer for agentic AI systems. Design and run experiments to evaluate performance improvements from semantic memory, using GraphRAG and enterprise knowledge graphs, while aligning software models with hardware development. Produce rigorous benchmarks and evidence for technical validation, fundraising, and pilot support.
Design and build production-grade machine learning systems for document classification and extraction, focusing on high accuracy and scalability across global identity documents. Work with LLMs and traditional ML models, optimize inference pipelines, and collaborate with cross-functional teams to enhance fraud detection and customer onboarding. Emphasis on technical leadership, system reliability, and mentoring within a squad-based engineering culture.
This role involves securing AI-first drug discovery platforms by designing risk frameworks, protecting ML model artifacts, and implementing security controls across distributed training and inference systems. The engineer will lead AI-specific incident response, develop guardrails for LLMs and autonomous agents, and ensure compliance with global regulations like the EU AI Act. Close collaboration with ML researchers and platform teams is essential to embed security into the full AI lifecycle while advancing AI safety standards.
We’re Aioi R&D Lab - an AI tech hub in one of the fastest-growing insurance companies. We research and develop AI systems that catapult insurance from a slow-moving, traditional past into a data-driven, technology-lead and society-defining future.We’re looking for dynamic,...
This role involves building and deploying production-grade AI-powered systems that integrate LLMs, RAG, and agentic workflows into financial services operations. The engineer will design scalable backend and frontend systems using Node.js, TypeScript, and React, while orchestrating AI tooling on AWS cloud infrastructure. Emphasis is placed on observability, reliability, and end-to-end ownership of features that deliver real-world value to financial institutions.
Lead the design and implementation of AI-driven risk decisioning systems, focusing on fraud detection, credit risk, and financial crime. Develop scalable, cloud-native platforms using event-driven architectures and real-time processing, while mentoring engineering teams and shaping technical strategy. Work within a regulated environment to translate compliance and risk requirements into robust, production-grade solutions.
Lead the end-to-end delivery of AI solutions, from strategy and proof of concept to production deployment, with a focus on LLMs, RAG pipelines, and AI agents. Collaborate across data, infrastructure, and business teams to integrate scalable AI tools into enterprise workflows and drive measurable operational impact.
This role involves building and owning end-to-end machine learning components within a generative AI platform, with a focus on retrieval systems, knowledge representation, and RAG pipelines. The engineer will design and implement systems for knowledge retrieval, vector indexing, and context engines that power enterprise AI agents. Work includes developing evaluation frameworks, integrating with enterprise data sources, and collaborating across ML, product, and infrastructure teams to deliver high-impact AI solutions.
This role involves developing and scaling a generative AI platform, with ownership of end-to-end feature development across backend, frontend, and AI components. The engineer will work closely with ML models and LLMs, build reliable distributed systems, and integrate with vector databases and AI infrastructure. Collaboration with product, ML, and infrastructure teams is key, in a fast-paced environment focused on delivering enterprise-grade AI solutions.
Design and deploy production-grade NLP systems and large language models tailored for financial services, with a focus on scalability, data governance, and regulated environments. Develop fine-tuned, low-latency AI workflows integrated into cloud infrastructure, while mentoring engineers and advancing evaluation methodologies for LLM outputs. Work across the full ML lifecycle from research to deployment in a remote-first, innovation-driven team.
