דרושים » הנדסה » Senior Software Engineer, Profiling Services

At our company, we build AI-powered vision systems that enhance safety and decision-making for some of the worlds largest vessels.
Our platform processes live video streams from multiple onboard cameras to provide real-time situational awareness, detecting and tracking marine objects, even in low visibility and highly congested environments. These systems directly support navigational decisions and help prevent collisions, reduce human error, and improve operational efficiency.
Our systems are already deployed across thousands of vessels and have processed hundreds of millions of nautical miles of real-world data, operating in unpredictable and safety-critical conditions.
This role sits at the intersection of AI and high-performance systems engineering, focused on solving real-world problems under strict constraints. You will work on systems where performance and reliability are critical and where improvements have a direct, measurable impact on real-world safety.
This is a senior, systems-focused role with end-to-end ownership over performance and reliability of production computer vision pipelines. You will define optimization strategies, identify bottlenecks across the system, and drive improvements under real-world constraints.
What youll do
Build and optimize real-time computer vision pipelines running on edge systems processing live maritime video streams (e.g, NVIDIA Jetson, Triton Inference Server)
Take models from research and turn them into production-ready, reliable components deployed on vessels
Profile and improve end-to-end system performance across: multi-camera video ingestion; preprocessing; inference; postprocessing
Identify and resolve bottlenecks across CPU, GPU, memory, and pipeline coordination
Make and justify tradeoffs between latency, accuracy, stability, and resource utilization
Design and implement robust data and inference pipelines (video -> model -> actionable output for crew)
Develop benchmarking and evaluation workflows to measure performance end-to-end and support release gating
Build and improve observability tools, including logging, monitoring, and debugging workflows for production systems
Define and maintain clear interfaces between research code and production systems
Work closely with research and backend teams to integrate new models into production systems
Continuously improve system efficiency and reliability under hardware and runtime constraints.

The MLIL DataPlane team is looking for a Senior Software Development Engineer to own the design and implementation of our inference data plane. We build the software that makes large models run efficiently on custom hardware - spanning model execution, memory management, data movement, and serving integration.
Our work covers the full inference path: integrating serving engines with custom hardware, developing high-performance compute kernels, enabling efficient data movement, and driving models from early validation through production. We operate at frontier scale with large distributed models.
This is a ground-up effort with rapidly evolving hardware and software. We need a senior IC who can write and optimize low-level code for custom hardware, validate model architectures end-to-end, build test and profiling infrastructure, and drive performance across the stack.

Key job responsibilities
- Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference.
- Implement and validate LLM architectures (decoder-only, mixture-of-experts) end-to-end - from PyTorch model definition through distributed execution on custom hardware.
- Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism.
- Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets.
- Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bringup.
- Own features end-to-end: from design through implementation, testing, and integration into the broader software stack.
- Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions.
- Mentor engineers, drive design reviews, and raise the engineering bar across the team.

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.

We are seeking a skilled software engineer to join our NPU software stack development team. This role involves developing high-performance GPU programming frameworks, runtime systems, and libraries for AI/ML workloads. You will be responsible for implementing, optimizing, and maintaining GPU software stack components to support distributed AI training and inference.
Key Responsibilities:
Identify bottlenecks, analysis and optimize in distributed NPU eco-system
Design and develop NPU memory management system
Design and develop optimized NPU development framework, execution path and debugging
Develop compatibility with AI frameworks (Triton, PyTorch, JAX)
Write high-quality, well-tested code with comprehensive documentation
Collaborate with other teams (Hardware, Network, QA, AI Framework Integration)
Participate in code reviews and technical design discussions

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 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.
Key Responsibilities
Design & Develop Core Components: Build, maintain, and optimize highly scalable, resilient distributed services, storage engines, or data-processing pipelines written in C/C++.
System Architecture & Resilience: Drive architectural discussions and implementations around high availability, data consistency, replication mechanisms, fault tolerance, and multi-node concurrency.
Performance Optimization: Optimize hot execution paths, low-level data structures, memory management, and I/O subsystems to guarantee high throughput and minimal latency.
Complex Debugging & Troubleshooting: Investigate and resolve intricate production issues spanning the application layer, distributed networking protocols, file systems, and operating system kernels.
End-to-End Ownership: Take full technical ownership of critical features-from ambiguous requirements and system design through implementation, rollout, and observability in production environments.
Teams
Storage Platform: Focuses on building a next-generation distributed storage platform handling petabytes of data across large clusters specifically designed to power AI, enterprise, and analytics workloads. Handling everything that touches the hardware and operating system aspects in a software defined storage system
Data Path: Focuses on engineering a highly distributed, latency-critical Hot I/O Data Path and Element Store engine. This role is responsible for the ingestion, state-of-the-art compression, encoding, and retrieval of multi-protocol data (files and objects) under massive concurrency and ultra-low latency requirements.
Database: Focuses deeply on core relational database internals, specifically designing low-level storage engines, B-Tree/LSM-Tree data structures, MVCC concurrency control, and query execution planners.
Kernel: Focuses on the lowest software layers, emphasizing Linux Kernel development and block-level storage/file system engineering.
Protocols: Focuses strictly on engineering high-concurrency data/metadata paths that replicate external AWS S3 object-storage behavior and correctness under heavy retry and failover pressure.
Cloud: Focuses on cloud-native storage deployment, adapting and scaling complex high-availability storage infrastructure across major hyper-scaler cloud environments (AWS, Azure, GCP).
Compute Kafka: Focuses on distributed event-streaming and messaging platforms, specifically building a high-scale, exactly-once broker compatible with the Apache Kafka wire protocol.

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.

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 Staff Software Engineer - C++ Endpoint Security, Collection Engineering (Data Protection Team), you will be tasked with designing and implementing low-level C++ or Rust agent modules as a foundational member of our newly formed Data Protection unit. You will research and develop high-fidelity sensors and robust, scalable code to monitor and prevent sensitive data leakage across multiple operating systems. By building these mission-critical collection engines, you will play a key role in defining the technical architecture for our companys next generation of data-centric security.
What will you do?
Primary responsibilities include:
Lead, design and implement low-level agent modules (using C++ or Rust), capable of monitoring data access and movement with minimal performance overhead.
Research and evaluate technologies for building high-fidelity sensors that track data access.
Develop robust, scalable, and performant code that operates reliably across multiple operating systems and environments.
Collaborate closely with Core Agent, Backend, and Frontend teams to deliver a unified, user-facing, next-generation data protection product.

As a Senior or Principal Software Engineer in Cortex Cloud, you will contribute to the development and scaling of cloud-native security solutions for enterprise organizations. This role involves working within an established team to evolve a high-traffic product, with a focus on refining architecture, optimizing the technology stack, and maintaining engineering standards.
Your responsibilities include writing reliable code, influencing product direction, and designing distributed systems. You will be expected to make technical decisions that impact the long-term stability and performance of cloud workload protection services.
AI Integration & Engineering Workflow
A core component of our development process is the use of AI. Rather than basic code completion, we integrate AI assistants as functional components of our workflow. Our team utilizes a multi-agent AI system (IDEX/ProDex) that assists across the development lifecycle: from planning and architecture to code analysis and security reviews.
In this role, you will:
Work with AI Tools:Utilize platforms such asGemini, Claude, and Cursorfor tasks beyond code generation, including root-cause analysis, system design reviews, and architectural assessment.
Develop AI-Augmented Workflows:Help refine how AI is integrated into the SDLC, including the orchestration of agents and the development of internal tools that extend AI capabilities across our codebase.
Maintain Quality Standards:While AI assists in increasing velocity, you are responsible for the technical output. This includes critical review of all generated code and ensuring that AI-assisted work aligns with our architectural requirements and security benchmarks.
Interact with Specialized Agents:Coordinate with AI agents (Product, Architecture, Security) that operate on shared context to assist in managing complex engineering tasks.
We are looking for engineers who are interested in leveraging AI as a technical tool to manage complexity and who want to contribute to the practical application of human-AI collaboration in a cloud environment.

As a Senior or Principal Software Engineer in Cortex Cloud, you will contribute to the development and scaling of cloud-native security solutions for enterprise organizations. This role involves working within an established team to evolve a high-traffic product, with a focus on refining architecture, optimizing the technology stack, and maintaining engineering standards.
Your responsibilities include writing reliable code, influencing product direction, and designing distributed systems. You will be expected to make technical decisions that impact the long-term stability and performance of cloud workload protection services.
AI Integration & Engineering Workflow
A core component of our development process is the use of AI. Rather than basic code completion, we integrate AI assistants as functional components of our workflow. Our team utilizes a multi-agent AI system (IDEX/ProDex) that assists across the development lifecycle: from planning and architecture to code analysis and security reviews.
In this role, you will:
Work with AI Tools: Utilize platforms such as Gemini, Claude, and Cursor for tasks beyond code generation, including root-cause analysis, system design reviews, and architectural assessment.
Develop AI-Augmented Workflows: Help refine how AI is integrated into the SDLC, including the orchestration of agents and the development of internal tools that extend AI capabilities across our codebase.
Maintain Quality Standards: While AI assists in increasing velocity, you are responsible for the technical output. This includes critical review of all generated code and ensuring that AI-assisted work aligns with our architectural requirements and security benchmarks.
Interact with Specialized Agents: Coordinate with AI agents (Product, Architecture, Security) that operate on shared context to assist in managing complex engineering tasks.
We are looking for engineers who are interested in leveraging AI as a technical tool to manage complexity and who want to contribute to the practical application of human-AI collaboration in a cloud environment.