דרושים » AI » senior applied ai researcher, digital biology

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 seeking a Senior Applied Researcher/ Data Scientist to join our core product organization, where you will build and deploy next-generation AI models powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). The role centers on building and scaling intelligent systems powered by LLMs, advanced ML techniques, and agent-based architectures.
You will join a team within our Cyber R&D organization, leading the companys core product portfolio-while driving AI innovation and setting best practices across the domain. The group focuses on developing next-generation technologies, including training and optimizing LLMs and building advanced multi-agent workflows, while mentoring data scientists across R&D.
As a key technical leader, you will guide the evolution of our AI capabilities-converting cutting-edge ideas into impactful, production-ready systems and shaping our long-term direction.
We are looking for visionary applied researchers eager to take on ambitious challenges and push the boundaries of AI in cybersecurity.
Responsibilities
Design and build advanced AI models and systems that power our core products and address real-world cybersecurity challenges.
Own initiatives end-to-end from identifying customer-driven problems and shaping solutions to experimentation, prototyping, and production deployment.
Drive forward new ideas and research directions, turning emerging concepts into practical, scalable solutions.
Influence the organizations roadmap and contribute to long-term technical strategy across our AI and product landscape.

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

We are looking for a talented AI Applied Researcher to help us develop advanced AI solutions that will empower impactful projects. The Data Science team leads the AI research and innovation efforts of the company, and is focused on pushing AI boundaries to enhance the product and optimize processes.
What Youll Do:
Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions
Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails and audit trails.
Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
Collaborate with engineering teams to design, build, and maintain production pipelines.
Stay up to date with the latest developments in AI, machine learning, and related fields, focusing on LLMs, exploring how emerging technologies can be applied to improve products and services.

As an AI Researcher, youll be part of the team building the AI systems that power our products. Youll work across research and engineering - from training and post-training state-of-the-art LLMs to applying them to challenging real-world problems such as agentic behavior, coding, and security-related decision-making.

This is a hands-on research role with a strong focus on taking ideas from experimentation to production. Youll have significant ownership and the opportunity to shape both the direction of our AI research and the systems we build around it.

WHAT YOU WILL DO
Train, adapt, and scale state-of-the-art LLMs using techniques such as post-training SFT, knowledge distillation, LoRA, and reinforcement learning.
Develop and evaluate new algorithms and approaches, bridging cutting-edge academic research with production-grade AI systems.
Optimize models for production, balancing quality, latency, throughput, and cost at scale.
Own the AI lifecycle-from algorithmic prototyping and synthetic data generation to evaluation, deployment, and iteration of production AI features.
Work closely with engineering and product teams to translate research advances into impactful AI capabilities.
Lead research initiatives that advance the capabilities of our AI systems.

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 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.

Required Senior Applied AI Researcher- Sovereign AI Research
About The Position
Nations are waking up to a hard truth: critical intelligence infrastructure built on hyperscaler black boxes isn't a solution it's a dependency. The Sovereign AI Research Group exists to answer that differently.
We're not fine-tuning what already exists. We're rethinking the models architecture from the ground up modular, composable, and built with compute governance as a first-class design constraint, not an afterthought.
We operate under real-world constraints. The interesting problems live at the intersections of disciplines. That's where we operate.
This is a hands-on research role embedded within a team of senior researchers. Day to day includes training models, running benchmarks, synthesize data. The researchers you'll work alongside will challenge you technically and invest in your growth.
Responsibilities:
Open Research Tracks
Familiarity with at least one is expected:
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Train and evaluate models across research tracks, iterating fast while documenting rigorously.
Build and maintain benchmarking pipelines and evaluation suites.
Curate, structure, and preprocess datasets; contribute to synthetic data generation workflows.
Run ablations and controlled experiments to support research hypotheses.
Reproduce and stress-test results from recent literature relevant to the group's work.
Collaborate across tracks and with engineering teams through to production handoff.

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.

Join our company, a stealth-mode cybersecurity startup building the next generation of Vulnerability Management and Exposure Management.
Were looking for a Senior AI Researcher (Cyber Security) to join our founding team and build AI-powered product capabilities that enable unprecedented effectiveness in vulnerability and asset management.
In this role, youll work hands-on with large-scale security data (assets, vulnerabilities, exposure context) and design production-grade AI systems that turn messy, complex information into clear, actionable insights.
Own the end-to-end AI lifecycle: from feature ideation and initial research, through data exploration and model development, to delivering reliable, production-grade capabilities. Validate solutions in controlled testing and continuously monitor, evaluate, and improve performance in production.
If you love combining real security problems with applied data science and AI driven problem solving, and want to have real impact in a rapidly evolving product - wed love to meet you.