דרושים » תוכנה » Research Engineer, Fundamental AI Research (FAIR) - Generative Models/LLM Acceleration

We are seeking a Research Scientist to join its Fundamental AI Research (FAIR) organization, focused on making significant advances in Code Generation and LLMs reasoning with specific focus on reinforcement learning, synthetic data generation and advanced scaffolding/agentic techniques. You will have the opportunity to work with a broad and highly interdisciplinary team of scientists, engineers, and cross-functional partners, and will have access to cutting edge technology, resources, and research facilities.
AI Research Scientist Responsibilities
Lead, collaborate, and execute on research that pushes forward the state of the art in agentic code generation
Work towards long-term ambitious research goals, while identifying intermediate milestones
Directly contribute to experiments, including designing experimental details, implement reusable code, running evaluations, and organizing results
Contribute to publications and open-sourcing efforts
Mentor other team members. Play a significant role in healthy cross-functional collaboration.

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.

We are seeking a Senior AI Engineer to join core product organization, where you will design, build, and scale next-generation AI systems powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). This role focuses on developing production-grade systems leveraging LLMs, advanced machine learning, and agent-based architectures.
You will join a team within Cyber R&D organization-leading the companys core product portfolio- while driving AI innovation and establishing engineering best practices across the domain. The team focuses on building and optimizing large-scale AI systems, including LLM-based solutions and advanced multi-agent workflows, working closely with data scientists and researchers to bring ideas into production.
The Responsibilities:
Design, build, and own end-to-end AI solutions- from data collection and preprocessing to model training, evaluation, and production deployment.
Optimize systems for performance, scalability, and reliability in production environments.
Collaborate closely with product, design, and engineering teams to identify and deliver AI-driven capabilities that address real customer needs.
Stay up to date with emerging AI/ML technologies, frameworks, and best practices, and apply them where they create real impact.
Work across the stack, contributing to backend systems and data pipelines that support large-scale AI applications.
Troubleshoot and resolve complex system issues, including performance bottlenecks, race conditions, and memory-related challenges.
Approach problems with a strong analytical mindset, delivering robust solutions while contributing to a high-performing, collaborative team environment.

Req ID: 29528

As a Machine Learning Scientist II, you will work within a cross-functional team of engineers and product managers to develop, evaluate, and deploy GenAI-powered solutions for scalable, customer-facing applications. Your work will focus on implementing agentic capabilities, contributing to evaluation frameworks, and delivering measurable business impact through data-driven experimentation.



Key Job Responsibilities and Duties:

Contribute to the design and development of end-to-end agentic systems, ensuring code quality and efficiency in production.

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Adhere to and promote established best practices in GenAI application development within the team.

Collaborate actively with team members, participating in code reviews, sharing knowledge, and contributing to a positive team environment.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

we are looking for an AI Researcher to help build the intelligence behind our AI security platform.
This is a hands-on research role focused on understanding how modern large language models and AI agents behave, identifying new attack vectors and protection techniques, and translating cutting-edge research into production-ready capabilities. The intelligence developed by this team is a core differentiator of our platform and a key driver of the company's success.
You will work at the intersection of machine learning, LLMs, and cybersecurity, partnering closely with AI engineers and product teams to develop technologies that directly power our product. We're looking for someone who enjoys understanding how neural networks and foundation models work, pushes the boundaries of AI research, and is excited to see their work deployed into production and used by customers.
What you will do
Research the behavior, capabilities, and limitations of modern large language models and AI agents.
Design novel techniques for detecting, evaluating, and proactively protecting against AI threats, prompt injection, model misuse, and emerging attack vectors.
Develop new approaches for securing AI systems and improving the intelligence that powers our platform.
Design and run experiments to evaluate model behavior, validate new ideas, and measure the effectiveness of protection techniques.
Build research prototypes and turn them into production-ready capabilities.
Work closely with Product and Engineering to translate research into scalable product features.
Stay current with advances in LLMs, foundation models, post-training techniques, AI security, and AI safety.
Contribute to the long-term technical direction of our company's AI security platform.

We are seeking a highly accomplished AI Researcher to be part of the development of next-generation Foundation Models and Generative AI systems. In this role, you will bridge the gap between cutting-edge academic research and production-grade AI infrastructure. You will build, design, and fine-tune large-scale architectures (including LLMs, Multimodal, and Tabular Foundation Models) while applying them to complex, real-world domains like agentic behavior, coding and decision making.
In your day-to-day, you will:
Conduct pioneering research in deep learning and generative AI, specializing in Foundation Models, NLP, alignment, and multimodal learning
Deal with the impact of the models on production traffic, cost quality and latency
Train and scale state-of-the-art architectures using techniques like Post-Training SFT, Knowledge Distillation, LoRA, and sparse/hybrid model architectures
Own the AI lifecycle-from algorithmic prototyping and synthetic data generation to deploying production-grade AI features in fast-paced or big-data environments
Drive research initiatives, author publications/patents, and collaborate with other research teams to integrate intelligent capabilities into core products.

Are you a master of the AI lifecycle who can thrive at the intersection of deep learning research, cybersecurity and large-scale production systems? Do you possess the rare ability to jump into a complex technical crisis, diagnose a bottleneck in a Small Language Model (SLM), and lead a team to a production-ready solution?
As a Distinguished AI/ML Architect, you will be the premier technical authority across the Cortex AI/ML organization. You will serve as the "force multiplier" for multiple AI & ML teams, ensuring that our AI strategy - from endpoint-deployed DL/ML models to cloud-scale agentic products - is executed with world-class precision. You will operate as the primary technical architect and hands-on leader for our most ambitious and difficult initiatives.
Key Responsibilities
Operate at the forefront of AI and cybersecurity, leveraging vast datasets to design and deploy innovative defense mechanisms.
Provide horizontal technical leadership across endpoint, cloud, and agentic AI teams to ensure architectural excellence.
Oversee the design and deployment of different model architectures to guarantee scalability and high-performance standards.
Spearhead high-difficulty initiatives and new research frontiers, moving them from ambiguity to production.
Resolve complex technical bottlenecks across the stack, optimizing model efficiency and runtime performance.
Align research and engineering efforts to transform advanced models into robust, production-grade products.

Req ID: 29223

As a Machine Learning Scientist, you will design, build, and deploy advanced models that guide pricing and promotional optimization across our company. You will work closely with other scientists, engineers, analysts, and product teams to translate complex business challenges into scalable, data-driven solutions that deliver measurable impact.

Key Job Responsibilities and Duties:

Develop and deploy models for causal inference, uplift estimation, and optimization to measure and maximize the incremental effect of price and promotion decisions.

Design and improve dynamic pricing algorithms that balance competitiveness, conversion, and profitability.

Contribute to the development of platform capabilities, enhancing experimentation, simulation, and decision-support capabilities.

Partner with product and business stakeholders to translate scientific insights into actionable strategies.

Stay up to date with the latest advances in machine learning, causal modeling, and pricing optimization, and apply them pragmatically at scale.

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.

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.