דרושים » AI » Sr. Principal AI Researcher, AI Transformation

our israel is seeking innovative and driven applied researchers in the field of deep learning. we are looking for candidates with a consistent track record of research excellence; a broad perspective across the fields of artificial intelligence; and deep experience in Machine Learning, applied science, and computational medicine. we are a world leader in high-performance and mobile computing technology for ai, with ambitious plans for future systems. this position offers the opportunity to have a real impact in an applied research-focused team in a dynamic company.
the applied ai architecture group focuses on applied research. as a member of the team, you will publish your work and share code as an open source. in addition, you will join a groundbreaking initiative at the intersection of ai and biology. this collaborative project aims to develop both foundational and generative ai models, as well as agentic ai systems, redefining our understanding of complex biological systems and enabling advanced diagnostics and novel scientific insights, while strengthening our health platform ecosystem.
what you'll be doing:
conceptualize, design, and implement novel deep learning architectures for biological data, with a strong focus on large-scale models such as large language models (llms), transformers, and state space models (ssms).
develop multimodal learning systems that integrate heterogeneous data types (e.g., clinical time-series, imaging, genomics, and text) for improved representation and prediction.
develop both foundational and generative models and agentic ai systems, including multi-step reasoning, tool use, and autonomous decision-making capabilities.
develop digital twin systems for healthcare, combining mechanistic models, physiological data, and ai to simulate disease progression, treatment response, and patient-specific trajectories.
implement deep learning systems integrated with agents, enabling end-to-end workflows that combine learning, planning, and execution.
evaluate model performance, analyze results, and iterate on designs to achieve optimal outcomes.
apply your knowledge of distributed training to build high-quality code for training, optimizing, and deploying large-scale models, while managing complex datasets.
collaborate closely with a diverse team of researchers, bioinformaticians, and domain experts in a highly interdisciplinary environment.

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.

We are looking for an exceptional researcher at the intersection of artificial intelligence and cybersecurity. This role is intended for someone who combines deep security expertise with a practical understanding of modern AI systems and the curiosity to explore how they can be attacked, abused, and defended.
Key Responsibilities
Research vulnerabilities, attack techniques, and emerging security risks in AI models, agents, applications, and supporting infrastructure.
Develop innovative attacks, proofs of concept, evaluation methods, and defensive solutions.
Analyze how model behavior changes across problem domains, task structures, prompts, tools, resources, harnesses, and model architectures.
Translate research findings into clear security requirements, technical concepts, publications, and practical protections.
Present original research at leading international cybersecurity conferences.
Maintain a strong understanding of developments across the AI and cybersecurity landscapes.
Collaborate with security researchers, AI engineers, product teams, and other stakeholders to turn research into measurable impact.

We're looking for an AI Security Researcher with a strong AppSec foundation. At the core, this is a deeply technical application security role at an engineering level. You'll sit with development teams, get involved in their processes, understand how software is actually built, and bring a security perspective from the inside. You'll enhance traditional AppSec with agentic tools and security layers, working directly with enterprise customers on hands-on engagements. On top of that defensive foundation, you'll layer AI-specific offensive capabilities: researching and executing attacks against AI/ML systems.
This is a unique opportunity to combine strong AppSec fundamentals with cutting-edge AI red teaming and agentic consulting, with a clear growth path as a researcher.
Roles and Responsibilities:
Conduct deep, engineering-level application security work: review architectures, assess code, and embed with development teams as a technical peer throughout the development lifecycle
Lead AI-focused security assessments and red team engagements for enterprise customers, identifying vulnerabilities in AI/ML systems, agentic workflows, and LLM-based applications
Enhance traditional AppSec processes with agentic tools and AI security layers
Research and develop novel attack techniques targeting AI/ML systems, including prompt injection, model manipulation, and agentic framework exploits
Translate offensive findings into actionable security recommendations for customers and internal teams
Stay at the forefront of AI security: continuously research emerging attack surfaces and contribute to the growing body of AI threat knowledge.

We are building a new AI venture inside monday.com to redefine how businesses interact with their customers through next-generation conversational AI and agentic systems. Our AI doesnt just react - it plans, reasons, and acts.
Youll join a small, highly autonomous team with the agility, ownership, and pace of a startup, working alongside world-class engineers and researchers to build the next generation of conversational AI. Youll have access to large-scale real-world data, the compute and resources to pursue groundbreaking research, and the freedom to move fast while helping shape Harmonys AI vision.
What youll do
Research novel algorithms and design new deep learning architectures to advance our in-house NLP and conversational models.
Develop methods for translating customer goals into structured representations with controlled states and real-time observations.
Help design the reasoning layer that orchestrates our AI models efficiently alongside a range of LLMs, enabling each agent to adapt its policy throughout a conversation while remaining aligned with customer objectives.
Build analytical tools that trace our AI decision process across a vast corpus of conversations, providing clear visibility into their reasoning and behavior.
Collaborate closely with engineers and product teams to translate cutting-edge research into production at scale.
Proactively lead new ideas and technical initiatives.

We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar

our mission is to make the pursuit of creative hobbies simple and fun. our top-grossing apps put us on the fast track to building the worlds largest global consumer subscription service thats revolutionizing how we spend our time at home. with over a million monthly downloads and hundreds of thousands of daily learners around the world, were redefining creative learning at scale.
what senior ai research engineers do here:
senior ai research engineers on our team develop algorithmic solutions that power new innovative features and continuously improve existing ones, with a strong emphasis on training ai / ml models as a core part of the role. the work spans a wide range of domains - from training generative ai and llm-based models to create musical content at scale, through building deep learning computer vision models that enhance user drawings, to developing Real-Time audio ml models for music transcription that elevate feedback.
you will take full end-to-end ownership of cutting-edge algorithms and ai models - leading the process from ideation and research, through development, to production-grade implementation and engineering within our products. this includes not only designing and building ai models, but also ensuring robust, efficient, and scalable integration into the app - directly shaping the experience of millions of users.

This is a research‑first role focused on deeply understanding LLM internals to improve the security of AI agents. Youll design careful experiments on activations and interpretable features- e.g., probing, attribution & ablation/patching, representation‑geometry analyses-to uncover mechanisms behind jailbreak, indirect prompt injection, and other attacks. Then translate those insights into signals that can be used for detection and analysis of a model response.

The field of LLM interpretability at scale is exploding, with several major publications in the last months, and major opportunities for innovation.

Responsibilities:
Investigate model internals, including activation/features analysis, unsupervised clustering, discovery of directions in latent space, etc. It may also require training specific model parts to improve interpretability metrics.
Design security‑grounded evaluations: curate datasets for different attack types, evaluate performance of different white box (model internals) methods compared to black box (input/output only) baselines.
Publish and share: produce our Labs posts and open artifacts; when the work is strong, aim for tier‑1 ML venues (NeurIPS, ICML, etc.) and security forums. A publication of code and/or trained models in cases of community relevant novelty.
Build tools: Several open source libraries exist (like Anthropics attribution graphs infra), but the research in the field is very dynamic, which will require you to build and adapt tools to your own research directions. This also includes agents to automate research work and distill knowledge from designed experiments.

We are looking for an Applied AI Researcher to join our multidisciplinary Antibody team composed of computational biologists, mathematicians and protein designers. In this role, you will drive the development and application of advanced machine learning methods to design and optimize antibodies, contributing directly to cutting-edge therapeutic programs and to the evolution of Scalas computational platform.

Develop and apply cutting-edge AI/ML methods for antibody design, including generative modeling and protein language models

Design and implement algorithms for multi-objective optimization of antibodies (e.g., affinity, specificity, stability, developability)

Build tools to support iterative design cycles, combining computational predictions with experimental feedback

Collaborate closely with experimental and computational teams to translate models into impactful design outcomes

Stay current with advances in machine learning, structural biology, and AI-driven protein design

We are looking for a hands-on AI Platform Team Lead to build and lead the team behind this platform: a high-throughput, low-latency engine that runs GPU-based models, from MMBERT-style models to LLMs, together with CPU-based heuristics and security logic.
This is a core infrastructure role for someone who wants to own the runtime layer of AI security at scale: performance, reliability, orchestration, GPU efficiency, and production-grade execution in the traffic path.
The team will also own the model lifecycle required to take AI security algorithms from research to large-scale production, working closely with research and algorithm teams.
Responsibilities:
Build and lead our AI Platform team: hiring, mentoring, architecture, technical direction, and execution.
Own the AI security runtime platform for high-throughput, low-latency inline security decisions across our global cloud and PoPs.
Design the orchestration layer for running GPU models, CPU heuristics, and security logic as one production engine.
Own production readiness: observability, SLOs, autoscaling, reliability, rollout, rollback, and operational health.
Own the model lifecycle platform: registry, versioning, deployment, monitoring, and safe production rollout.
Work closely with research and algorithm teams to productionize AI security models and algorithms at scale.
Define the long-term platform strategy for AI runtime and model serving.