דרושים » תוכנה » Software Engineer, AI Networking Architect

We are looking for a Host and Systems Performance Manager to join ur Networking Performance team!

In this role, you will lead a team of engineers who measure, analyze, and improve NIC, host, DOCA networking stack, and system-level performance across our networking products and platforms. We work across the full product lifecycle, from early architecture and pre-silicon modeling through post-silicon bring-up, validation, characterization, and GA readiness.

We are looking for someone who enjoys building teams, turning complex performance data into clear direction, and partnering across engineering groups to improve products that support AI, HPC, cloud, and accelerated networking workloads.

What Youll Be Doing:
Lead, coach, and develop a team focused on NIC, host, DOCA networking stack, and system-level performance. You will define performance test plans, methodologies, metrics, and success criteria for our new networking technologies.
Guide pre-silicon and post-silicon performance planning, analysis, reporting, and readiness reviews. Your team will benchmark and profile workloads across RDMA, RoCE, InfiniBand, Ethernet, MPI, NCCL, DOCA, storage, security, and host networking stacks.
Identify bottlenecks across NIC, DPU, CPU, memory, PCIe, firmware, drivers, Linux networking, DOCA, and full-system architecture. We will look to you to lead root-cause analysis, coordinate mitigation plans, and help hardware, firmware, software, architecture, validation, and product teams align on performance goals.

We are seeking a Director of Software Architecture to lead and accelerate the evolution of next-generation AI data center and networking technologies. This role is designed for a hands-on, execution-driven leader who pushes boundaries, translates vision into reality, and delivers high-impact solutions at scale. You will operate at the intersection of innovation, architecture, and delivery-shaping the future of AI networking and infrastructure.

What you will be doing:
Drive identification, evaluation, and rapid adoption of emerging technologies, ensuring strong alignment with strategic roadmap and measurable business outcomes.
Lead the design and delivery of advanced networking applications, leveraging data plane programming and modern networking protocols to solve complex, large-scale challenges.
Architect solutions across AI data center environments, integrating GPU-based systems, hardware acceleration, and high-performance networking.
Act as a thought leader and industry influencer-engaging directly with customers, publishing technical content, and representing us at key conferences and forums.
Define and execute a bold architectural vision for our networking in close collaboration with cross-functional software and hardware leaders.

We are seeking a highly motivated High-Performance System Architect to join our team of experts and help shape the future of high-performance and ML / AI computing. Our next-generation NVL systems will be at the forefront of connecting and powering the world's most advanced compute clusters, which would be used to train the most advanced AI models such as GPT and DeepSeek. As a high-performance system architect, you will have the opportunity to work on some of the most cutting-edge technology and help to drive the innovation of our next generation networks that will be used by top researchers and engineers around the world.

What youll be doing:

Define the NVL system architecture end-to-end, by internal requirements and customers requirements through all product life cycles (post/pre silicon, on deployments).

Research various of solutions to enable the next large-scale-high-performance computing clusters. The position spans over various layers from algorithms, software, firmware, and HW.

Collaborate with cross-functional teams, including other architecture teams, logic design, system software, firmware, and research teams, to ensure the successful execution of the project.

Our Networking System Product Engineering organization is looking for a Senior Data & AI Solutions Engineer - high-agency, self-directed Data & AI Solutions Engineer to partner with engineering teams and transform data, BI, automation and agentic AI into measurable engineering productivity gains.

What youll be doing:
Work closely with engineers, managers, and cross-functional teams to understand complex engineering domains, identify bottlenecks, and build practical solutions that improve decision-making, execution speed, and operational quality.
Develop and deliver production-grade tools, automated workflows, and agentic AI solutions from concept through deployment.
Run fast, high-quality proof-of-concepts on emerging AI and agentic technologies, and productize successful ones.
Implement data flywheels that continuously improve quality through telemetry, benchmarking, automated evaluation, and structured feedback loops.
Collaborate and contribute ideas and code to Product Engineering's evolving data infrastructure.
Improve data quality, integrity, governance, metric definitions, and usability across engineering domains.
Train and enable engineers, managers, and stakeholders to use data, BI, and AI tools effectively.

We are seeking a Network Solution Verification Engineer to join our R&D organization, focused on validating our customer-facing networking solutions at scale using a state-of-the-art End-to-End (E2E) simulation cluster environment.

In this role, you will be a hands-on technical contributor at the intersection of networking, automation, and AI-driven validation - designing and implementing the simulation frameworks, agentic workflows, and regression pipelines that ensure our networking solutions meet the highest standards of quality and real-world applicability before reaching customers.


What you'll be doing:
Designing and implementing end-to-end validation frameworks for our customer-facing networking solutions at scale, leveraging a dedicated E2E simulation cluster environment.
Writing, maintaining, and extending automated test suites and regression pipelines for networking protocols and large-scale simulation runs, ensuring repeatable, high-confidence validation outcomes.
Performing deep regression analysis on simulation results - identifying failure trends, isolating root causes, and delivering clear, actionable findings to architecture and design teams.
Developing agentic AI flows that autonomously perform regression analysis, detect coverage gaps, generate new test cases, and implement validation code - continuously learning from simulation results and product changes to accelerate coverage without manual intervention.
Integrating validation pipelines into CI/CD workflows to enable continuous, automated regression at scale, working closely with DevOps and platform teams.
Collaborating closely with Design, Architecture, and NCS teams to understand solution requirements, translate them into simulation scenarios, and provide early-cycle quality feedback that influences product direction.
Analyzing customer-reported networking issues, mapping them to simulation coverage gaps, and building targeted test cases that prevent regression.
Continuously exploring new simulation technologies, agentic frameworks, and networking standards to evolve and improve the team's validation methodology.

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.

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.

We are looking for a Technical Lead to drive the architectural direction and engineering excellence of this group. This is a senior, deeply hands-on role for a technology leader who can own the technical roadmap, mentor a team of elite engineers, and build the infrastructure that challenges platform to its theoretical limits.
What You'll Lead:
Define and own the technical architecture of the group's distributed testing and reliability platform - designing for massive scale, real-world workload simulation, and adversarial failure injection
Lead effort involving multiple engineers, setting technical standards, running architecture reviews, driving design decisions, and mentoring engineers to grow
Build the systems that orchestrate millions of concurrent IO operations, inject chaos at the infrastructure layer (latency, packet loss, hardware failures), and expose the hardest-to-find race conditions and consistency bugs
Advance AI-driven approaches to test automation: intelligent scenario generation, LLM-augmented root-cause analysis, and autonomous validation pipelines
Drive observability and reliability engineering across the group - building telemetry pipelines that track P99 latency, jitter, and system health, turning quality into a quantitative discipline
Collaborate deeply with Core R&D, Storage Kernel, and Infrastructure teams - translating architectural knowledge into targeted reliability strategies
Establish engineering practices - design docs, production-grade code reviews, testing philosophy, and cross-team technical alignment

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.