דרושים » תוכנה » Senior ML Software Engineer, Data Plane

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
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.

The MLIL FLOW team is looking for a Senior Software Development Engineer to lead the design and delivery of systems software for our next-generation ML accelerator servers. We build production software to validate, initialize, monitor, and qualify these servers - from first silicon through fleet-scale deployment. We work on the physical systems that execute ML workloads: Server bring-up, hardware diagnostics, interconnect validation, power/thermal monitoring, and fleet-scale operations are our bread and butter.


Key job responsibilities
Lead the architecture and implementation of hardware validation and diagnostic software for new ML acceleration platforms.
Drive technical direction for PCIe validation, power/thermal diagnostics, and stress-testing frameworks that run across manufacturing, vetting, and production environments.
Own subsystems end-to-end: from design through implementation, testing, deployment, and operational excellence at fleet scale.
Work with Hardware, Manufacturing, EC2 teams to create coordinated software packages that enable both qualification and rapid deployment.
Debug and root-cause complex hardware/software interaction failures on first silicon and production fleet returns; drive root-cause to closure.
Build and maintain data pipelines, dashboards, and monitoring systems for fleet health and performance benchmarking.
Mentor engineers, define best practices, drive design reviews, and raise the bar for the team.
Lead multiple development initiatives in parallel, balancing schedule, risk, and technical quality across a fast-moving hardware program.

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.

Our team is looking for a Deep Learning Engineer.
We are one of the few companies to have trained multi-billion parameter Large Language Models (LLMs), a feat that involves the most advanced engineering (large scale distributed training on thousands of cores). Serving these LLMs efficiently requires cutting-edge technology as well. As a deep learning engineer on the team, you will be responsible for maintaining and improving our training infrastructure, developing/scaling/testing new ideas, and adapting our code to run on and best utilize the newest and most advanced hardware accelerators.
Role and Responsibilities
Develop Large Language Models as part of our applied research projects and in support of our Platform, including designing, implementing and training massive-scale deep language models
Implement, optimize, scale and test new cutting edge ideas and architectures
Perform large-scale evaluations and comparisons of trained models across a range of benchmarks, as well as adding support for new benchmarks

We are seeking a talented and passionate Software Engineer to join our NIC team, working at the forefront of AI datacenter infrastructure. In this role, you will develop and optimize the software stack powering high- performance NICs deployed in large-scale AI training and inference clusters. You will collaborate closely with hardware, networking, and AI framework teams to ensure our NIC stack delivers maximum throughput, minimal latency, and seamless integration with GPU computing frameworks and collective communication libraries.
Key Responsibilities
● Design, implement, and optimize L2 and RDMA features for third-party NIC hardware
● Design and develop tools for real-time NIC performance monitoring, profiling, and telemetry in AI datacenter environments. Analyze end-to-end distributed training and inference bottlenecks.
● Develop, maintain, and extend NIC drivers and firmware interfaces within the Linux kernel network subsyste
● Collaborate in the evaluation and integration of upstream kernel, vendor, open-source NIC features,
Hardware, Networking, QA, and AI Framework Integration teams
● Implement, integrate and tune RoCEv2 / lossless Ethernet transport for AI workloads requiring ultra- low latency with GPU programming frameworks (CUDA, ROCm)
● Write production-grade, well-tested, and thoroughly documented code following team engineering standards and participate actively in code reviews, architecture discussions, and technical design sessions.

We are seeking an experienced Senior Delivery Consultant - Modernization with deep expertise in Artificial Intelligence to join AWS Professional Services (ProServe). This role combines strategic architectural vision with hands-on technical leadership to deliver innovative AI solutions that drive customer success and business transformation across diverse industries and use cases.

Key job responsibilities
* Architecture & Design: Design and architect end-to-end AI-powered application solutions aligned with customer business objectives and technical requirements.
* Define application architecture patterns, standards, and best practices for AI/ML integration on our company.
* Create technical roadmaps for customer AI application development and modernization initiatives.
* Evaluate and recommend our AI/ML services and technologies including Amazon Bedrock, SageMaker, and generative AI solutions.
* Design data pipelines and ETL processes to support AI model training and inference using our services.
* Customer Engagement & Consulting:
Lead customer engagements from discovery through implementation, serving as trusted technical advisor.
* Conduct AI readiness assessments and develop adoption strategies tailored to customer maturity levels.
* Facilitate architecture workshops and design sessions with customer stakeholders.
* Deliver Well-Architected reviews focused on AI/ML workloads.
* Build strong relationships with customer technical teams and executive leadership.
* Guide customers in constructing AI processes aligned with AWS best practices.
* Technical Leadership: Lead cross-functional teams in implementing AI solutions from concept to production.
* Provide technical guidance on AI model integration, deployment strategies, and optimization on our company.
* Conduct architecture reviews ensuring solutions meet scalability, performance, security, and cost-efficiency requirements.
* Mentor customer teams and junior ProServe consultants on AI best practices and AWS technologies.
* Collaborate with data scientists, ML engineers, and software developers to translate AI models into production applications.
* AI Solution Development: Design architectures for generative AI applications including RAG (Retrieval-Augmented Generation) systems, chatbots, and intelligent agents using our Bedrock.
* Architect real-time and batch AI inference pipelines with appropriate monitoring and observability.
* Implement MLOps practices using SageMaker for model versioning, deployment automation, and continuous improvement.
* Design solutions for responsible AI including bias detection, explainability, and governance frameworks.
* Optimize AI application performance, cost, and resource utilization across AWS services.
Knowledge Sharing & Thought Leadership:
* Develop reusable assets, reference architectures, and best practice documentation.
* Contribute to AWS ProServe knowledge base and customer-facing content.
* Present at customer events, workshops, and industry conferences.
* Stay current with emerging AI technologies and AWS service innovations.
* Share learnings across the ProServe organization.

Your Opportunity:
Serve as the primary technical authority and mentor for the team, providing expert guidance, performing code reviews, and setting the bar for engineering excellence.
Partner closely the leadership team to define and evolve the architecture of our end-to-end OT solution, spanning from network data collection to complex on-prem and cloud deployment scenarios.
Own architectural decisions and drive critical tech choices that ensure system scalability, reliability, and seamless integration with the broader our product suite.
Lead the systems evolution by shaping the target architecture and integrating AI-driven capabilities and advanced analytics to proactively identify network threats and failures.
Leverage complex cross-domain data and metrics to drive product performance and stability improvements.
Lead technical initiatives and discussions across Backend, Frontend, and other teams to ensure high-quality software delivery.
Collaborate with cross-functional partners, including product management and design, within an Agile environment to shape and deliver new features.

we are looking for a Machine Learning Engineer II .
As a Machine Learning Engineer, you will work closely with experienced engineers and ML scientists to build scalable, production-grade GenAI applications. Your work will focus on designing, training, and deploying ML systems leveraging LLMs,, recommendation systems, and agent-based architectures, using state-of-the-art technologies. These solutions will directly power customer-facing experiences and play a key role in shaping the future of AI-driven travel products.
Key Job Responsibilities and Duties:
Deploying machine learning models: Design, develop and deploy in collaboration with scientists, scalable machine learning models and algorithms that provide content related insights and generative AI applications, ensuring scalability, efficiency, and accuracy.
Evaluating possible architecture solutions by taking into account cost, business requirements, emerging technologies, and technology requirements, like latency, throughput, and scale.
Generative AI Development: Contribute to the development of generative models such as GPT (Generative Pre-trained Transformer) variants or similar architectures for creative content generation, Q&A, chatbots, translation or other innovative applications.
Deployment and integration: Work closely with software engineers to integrate machine learning models into production systems. Ensure seamless deployment and efficient model inference in real-time environments. Collaborate with DevOps to implement effective monitoring and maintenance strategies.
Owning a service end to end by actively monitoring application health and performance, setting and monitoring relevant metrics and acting accordingly when violated.
Maintain clean, scalable code, ensuring reproducibility and easy integration of models into production environments, including CI/CD.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

We are looking for senior engineers to help us define and build the future of computing. The Elastic Block Store (EBS) service delivers high performance virtualized storage "in the cloud" for EC2. Durable and performant storage is a key building block for any application and as a member of the EBS team you'll work alongside exceptional engineers and leaders to develop and operate one of the largest distributed storage systems in the world.

We are looking for a senior software development engineer for the storage- and durability-focused data plane group that owns core EBS logic and contributes to a broad and strategic set of projects. You will be part of a team of engineers that develop and maintain the components enabling EBS storage, high durability and performance.

Key job responsibilities
You will collaborate with and learn from the industry's best minds to solve interesting and challenging problems at an enormous scale on a daily basis. This collaboration extends beyond just the team in EBS. You will have the opportunity to work with many other teams in AWS to build a secure, performant, and reliable system.

In this role you'll design and build features that shape how customers discover products - tailored to their preferences, helping them find exactly what they need, even before they know they need it. We're defining how LLMs integrate into real-time personalization, how noisy behavioral signals become durable customer understanding, and how AI-powered experiences ship reliably at scale. You'll work across multiple technical teams, ship iteratively, and see your work in the hands of customers quickly. This organization values experimentation, moves fast, and gives engineers real ownership over what they build.

We are looking for a Software Development Engineer with sound technical judgment and a bias for action who takes ownership of problems end to end, communicates clearly, and cares about operational excellence - not just launching features, but making sure they hold up at scale. Someone who naturally raises the bar for the team: mentoring junior developers, advocating engineering best practices, and thinking beyond the immediate sprint.

Key job responsibilities
- Design, build, test, and operate production AI features used by multiple teams and operating in our scale.
- Deliver end-to-end solutions with focus on maintainability, scalability, performance, and reliability.
- Collaborate with Product and Science to define experiences, run experiments, and iterate based on data.
- Build AI-powered experiences including personalized recommendations, relevance explanations, and knowledge-driven features using LLMs and generative AI.
- Define and implement measurement strategies including analytics events and experiment configurations to track engagement and retention.
- Navigate ambiguity and make sound technical decisions in a problem space where established patterns don't always apply.