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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.

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.

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.

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

Required Research Engineer, Agents
About us:
We transform trade contractors' knowledge, documents, and workflows into AI-powered systems - so projects run the way their best people would. We're building agents that read drawings, ground claims in spec paragraphs, and orchestrate real workflows across Procore, Autodesk, and the project document library.
Key Responsibilities:
Design and ship agent architectures that solve real construction workflows: RFI drafting, submittal compliance, schedule Q&A, pay-app validation
Build evaluation pipelines and domain-specific test sets from real customer projects
Push the boundaries of long-context retrieval and multi-document grounding
Work directly with customers - superintendents, estimators, project managers - to validate and refine
Own a workflow end-to-end, from research idea to production rollout.

our company, a well funded startup developing AI for diagnosing diseases from retinal images, is looking for an AI (vision) Researcher.
As part of the team you will:
Develop novel solutions for retinal screening, spearheading healthcare accessibility
Help prevent blindness worldwide
Join a leading company with unique FDA approved products
Learn from top ML researchers in the field.

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 looking for a Lead Endpoint Researcher.
What You'll Do
Research AI coding agent runtimes, reverse-engineer how tools such as Claude Code, Cursor, Copilot CLI, Codex, and emerging agents execute commands, access files, spawn processes, and interact with the operating system.
Design agent sandboxing architectures that safely constrain filesystem access, networking, process execution, credentials, and other sensitive endpoint capabilities.
Explore and implement OS-native isolation mechanisms across macOS, Windows, and Linux, including containers, namespaces, sandbox profiles, virtualization, restricted processes, and permission models.
Build endpoint "harnesses" that allow TokenShift to intercept, control, observe, and modify agent execution flows.
Design infrastructure for deploying and running local LLMs across heterogeneous endpoint fleets, accounting for operating system, CPU architecture, GPU availability, memory constraints, and hardware acceleration.
Evaluate local inference runtimes and model formats such as llama.cpp, MLX, ONNX Runtime, Ollama, vLLM-compatible runtimes, and emerging alternatives.
Develop mechanisms for model discovery, installation, versioning, updates, health monitoring, and lifecycle management across large numbers of developer machines.
Research hardware-aware model selection and routing, deciding which models can run effectively on Apple Silicon, NVIDIA GPUs, Windows workstations, Linux machines, CPU-only environments, and other endpoint configurations.
Build benchmarks that measure model latency, throughput, memory consumption, startup time, resource contention, and developer experience on real endpoint hardware.
Investigate security boundaries between local models, cloud-hosted models, agents, MCP servers, shells, and enterprise resources.
Collaborate with the core CLI and platform teams to take endpoint research from prototype through production.
Track the rapidly evolving AI agent, sandboxing, and local inference ecosystems and proactively identify technologies should support.

We're looking for an Experienced Performance Researcher to join our founding team. Youll be responsible for building and optimizing scalable cloud infrastructure solutions tailored for AI workloads. This role offers a unique opportunity to directly shape our infrastructure strategy, improve system reliability and performance, and contribute to establishing us as a leader in adaptive AI compute management.
Join us to tackle the magic that make AI tick under the hood and build the backbone powering the AI revolution.
What Youll Do
- Design and build high-performance distributed inference pipelines for LLMs, focused on large-batch, non-real-time scenarios.
- Optimize GPU memory usage, kernel execution, and communication across nodes (NCCL, MPI, etc.).
- Own CUDA kernels, compiler-level tricks, and multi-GPU scheduling logic.
- Lead profiling and performance tuning for throughput, and cost- down to the kernel level.
- Collaborate with infra, product, and research teams to define SLAs, resource allocation logic, and runtime behaviors.
- Help build the core infrastructure that will run LLM workloads across hybrid GPU environments (cloud/on-prem/self-hosted).

We're looking for a talented AI Security Researcher to join our team and play a critical role in our company's foundational, risk-driven approach to cloud security. This role requires deep technical research into complex cloud- and AI-native environments to identify the most significant, unaddressed risks.
Responsibilities
Conduct deep technical research to discover and report novel risks and attack vectors specific to modern cloud- and AI-native architectures and systems.
Discover and articulate the highest unaddressed risk areas, working with Product and Engineering teams to translate research into product capabilities.
Define necessary foundational product capabilities by delivering both compelling proofs of risk (demonstrating impact) and technical POCs (showing how to solve it).
Work closely with Product and Engineering teams to ensure comprehensive risk coverage and support the investigation of new and complex product scope.

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 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.

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.

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.