we are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless workers who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

Were looking for a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do

Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.

Shape the Future of AI Infrastructure with our company
This is a rare opportunity to join one of the fastest-growing infrastructure companies in tech history, right at the epicenter of the AI revolution.
Recognized by Forbes as "the future of the market," we are building the foundational enterprise software powering the AI era. We engineer platforms that capture, catalog, refine, and protect massive datasets across data centers, edge, and cloud, making real-time analytics, AI training, and inference simpler and faster than ever before.
Our explosive growth is fueled by relentless engineering innovation, a customer-first mindset, and a team of fearless workers who thrive on solving computing's hardest problems. Join us at this pivotal moment in technology history and make a lasting impact on how the world processes data.
We are seeking a talented and experienced Software Engineer to design, build, and optimize high-performance distributed systems, core data engines, and backend infrastructure. This role requires deep system-level architecture understanding, the ability to handle large-scale clusters processing petabytes of data, and mastery of modern C/C++ and Linux environment internals.
What You'll Do:
Design and develop core components of a next-generation distributed storage platform.
Build scalable and resilient distributed services handling petabytes of data across large clusters.
Participate in architecture discussions around consistency, replication, fault tolerance, and system scalability.
Investigate and resolve complex production issues spanning storage, networking, distributed services, and operating system layers.
Collaborate closely with architects, researchers, and engineering teams to deliver high-performance data infrastructure powering AI, analytics, and enterprise workloads.

We are seeking an AI Networking Architect to join the Networking Research Group. This role will help bridge the gap between emerging tasks supported by advanced technologies and the data center infrastructure that powers them. In this role, you will work at the intersection of AI applications, distributed systems, networking hardware, and software architecture.

You will join a focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding, network analysis, and end-to-end systems thinking. Your insights will directly shape our products across the full stack - from applications and software libraries to hardware architecture and physical design.

What Youll Be Doing:

Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.

Analyze brand-new AI models, distributed training techniques, and inference workloads to understand their infrastructure requirements.

Build Platforms, simulations and HW platforms, execute AI workloads and build analytical tools to evaluate trade-offs across compute, memory, storage, and network behavior.

Translate research insights and workload behavior into actionable software, hardware, and networking architecture requirements.

Partner with architecture, software, and product teams to influence our future networking and AI infrastructure roadmaps.

Drive architectural innovation by applying deep workload analysis to real-world advanced machine learning frameworks.

We are seeking a highly skilled and versatile Performance Research and Analysis Manager to join our Performance Group. This role will drive end-to-end performance strategy and execution for next-generation our data centers and solutions based on GPU systems, NIC, Switch, DPU and Networking technologies. The ideal candidate will oversee, evaluating, and optimizing end-to-end AI GPU cluster-level performance for scaling out large scale distributed training and inference jobs communication. The role will focus heavily on RDMA, Networking Protocols, Collective Communication, Congestion Control, and Load Balancing algorithms. Secondarily, you will lead our DPUs and Storage technologies for N-S use cases to support AI Inference jobs. Third, you will drive our Performance Dashboards and Observability for cluster-level performance analysis from a stream line telemetry across NICs, Switches, GPUs, and NVlink.

What you'll be doing:

Drive end-to-end performance strategy, characterization, test plans, and optimization for next-generation our AI GPU clusters, focusing on large-scale distributed training and inference workloads.

Deeply evaluate and optimize our Networking core technologies performance, including RDMA/PRDMA, networking protocols, collective communication (NCCL), congestion control, and load-balancing algorithms.

Work on performance research and analysis of NVIDIA DPUs and storage technologies in North-South (N-S) use cases and deployment scenarios to maximize performance and efficiency for AI inference jobs.

Drive the strategy for performance observability and dashboards across next-generation NVIDIA data center solutions and supercomputers by leveraging scalable, streamlined telemetry pipelines to build performance dashboards and automated analytics based on real-time performance metrics across NICs, Switches, GPUs, and NVLink boundaries.

Perform deep root-cause analysis (RCA) on complex multi-node performance bottlenecks, driving actionable mitigation plans across hardware, firmware, and software teams.

We seek a versatile Senior Software Engineer who is passionate about performance optimization and generative AI. Our team brings the latest research in LLM inference - from novel decoding strategies to quantization schemes - into production across our hardware lineup, from large data center servers to powerful edge devices. We work on the most advanced architectures in the field, with a focus on NVIDIA's own.

What you'll be doing:

Implement and optimize inference algorithms for LLM and omnimodal architectures, including hybrid Mamba-Transformer and mixture-of-experts models.

Profile inference pipelines using NVIDIA's profiling and simulation tools. Correlate simulation predictions against real hardware across data center and edge devices.

Write and tune GPU kernels (CUDA, Triton) for operators like fused MoE layers, SSM state updates, and quantized GEMMs.

Solve distributed inference problems: expert parallelism, communication-compute overlap, collective tuning, multi-node deployment.

Build production-grade software inside major open-source libraries - vLLM, SGLang, Dynamo, FlashInfer.

Own optimization features end-to-end, from scoping through delivery, collaborating with research, product, and engineering teams worldwide.

Were looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.
As a Senior Staff Software Engineer in the Detection Platform group, you will be tasked with being the technical authority responsible for defining and evolving the architecture of the cloud-native systems that power our AI SIEM detection, hunting, and response capabilities, including large-scale real-time detection engines, stateful detection engines, anomaly detections, ML pipelines, agentic SOC and threat-hunting capabilities. You will lead the design and execution of backend systems that process billions of events and several petabytes of data daily and serve tens of thousands of security specialists at enterprise and government customers worldwide. Your technical leadership will bridge long-term architectural strategy and high-velocity product delivery, and you will drive cross-team initiatives that shape how detection and response are built and operated across the group.

We're looking for a Software Engineer to join our core engineering team and help build the infrastructure behind one of the fastest-growing AI APIs. You'll work on systems that handle massive scale across a distributed microservices architecture running on AWS. You'll ship fast, take ownership of critical systems, and solve hard infrastructure problems as we grow.

This is a great role for an engineer who loves building ambitious systems from scratch, and wants to tackle the kind of scale and complexity challenges typically reserved for much larger companies.

What Youll Do

Design and build high-performance distributed systems

Design and implement backend infrastructure and API endpoints

Build and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processors

Improve performance, monitoring, and reliability across the stack

Own core systems and contribute to key architectural decisions

Help shape a strong engineering culture focused on velocity and quality

we are looking for a Senior II Software Engineer to join the CX Platform team, the foundational engineering team powering entire Customer Experience group. This is a high-impact, hands-on role at the intersection of backend engineering, AI infrastructure, and customer-facing product.

You'll work across the full platform stack (backend services, data pipelines, security, cost, and scale) with a meaningful and growing focus on AI infrastructure. We own the agentic platform for the entire Product Offering group: from building the LLM infrastructure and agentic workflows to ensuring they're reliable, observable, and safe in production.
What you'll be doing:

Own AI infrastructure for the Product Offering group. Design, build, and evolve the shared AI platform (agentic workflows, LLM integrations, observability, and guardrails) that CX product teams build on.
Ship agentic features end to end. Lead development of AI-driven capabilities using LangChain, LangFuse, and AWS Bedrock, from architecture through production deployment and monitoring.
Drive platform architecture. Set the technical direction for the CX backend (services, data pipelines, API patterns) with an eye for scalability, reliability, and developer experience.
Own core data foundations. Design resilient data-access patterns across Snowflake, Elasticsearch, Kafka, Redis, and MySQL; keep pipelines fast, fresh, and reliable.
Mentor and elevate. Help engineers across the CX group grow in backend craft, AI engineering, and system design thinking.
Collaborate cross-functionally. Work with product, design, and customer-facing teams to turn ambiguous problems into well-scoped, high-quality solutions.