דרושים » AI » Visual GenAI Research Expert

Required Senior AI Researcher - Visual GenAI
About the team:
How do you generate a driving scene that stays consistent over time, across cameras, and under controlled changes? These are exactly the types of problems we solve.
Our Visual GenAI team develops image and video generation capabilities for autonomous driving, with a growing focus on video and world models.
Were looking for a hands-on researcher to help shape our research direction and turn ideas into working capabilities, with access to large proprietary driving datasets and close collaboration with the teams developing our next generation of autonomous driving technology.
What will your job look like:
Research and develop image and video generation models, with a focus on temporal consistency, multiview coherence, and controllable driving-scene generation.
Explore and adapt state-of-the-art world models for autonomous driving, helping shape the teams research and technical direction.
Own the full development lifecycle, from research and experimentation to model training, evaluation, and integration into real-world workflows.
Build scalable, clean Python and PyTorch implementations for training and evaluating generative models.
Collaborate with AI researchers and perception teams to develop generative capabilities for training and evaluating autonomous driving systems.

We are seeking a highly motivated AI Researcher to join our core Data Science team. In this role, you will be at the forefront of tackling complex, high-impact business challenges by leveraging cutting-edge GenAI technologies, advanced AI research, and classical machine learning and statistical modeling.
You will not just be building models; you will be driving research from ideation to production. We are looking for a true self-learner, someone who thrives in an environment that demands both a strong sense of ownership over your deliverables and deep collaboration. You will work closely with fellow researchers, product managers, and software developers to translate abstract business problems into scalable data-driven solutions.
If you are passionate about staying ahead of the AI curve, building agentic workflows, and proactively driving your research initiatives forward within a collaborative team structure, this is the perfect role for you.
What You Will Do:
End-to-End Research & Ownership: Lead targeted research projects from initial hypothesis through to production-ready solutions. Take strong ownership of your work's performance and partner with engineering to ensure successful, scalable integration
Applied AI Research & Integration: Lead research into LLMs, agentic systems, and modern AI methods, then design and build the workflows that bring them into our ecosystem.
LLM Evaluation & Optimization: Contribute to and establish rigorous evaluation frameworks for LLMs to ensure accuracy, safety, and business alignment in practical applications.
Cross-Functional Collaboration: Act as a bridge between data, engineering, and product. Work with Developers to ensure seamless integration of ML features into the core product.
Continuous Innovation: Act as an internal advocate for emerging AI research and methodologies. Continuously read, experiment, and implement state-of-the-art techniques to advance our research agenda and accelerate research velocity.

We are seeking a Senior AI Researcher to lead post-training evaluation, red-teaming, and reinforcement learning (RL) gym audits on open-weight models. The ideal candidate will establish rigorous benchmarking methodologies, evaluate large language models (LLMs) against complex threats like Indirect Prompt Injections (IPI), and construct post-training evaluation pipelines that accurately measure realistic frontier-level security capabilities.
Key Responsibilities
RL Post-Training & Benchmarking
Execute post-training runs (e.g. GRPO) using mainstream open-weight generalist models against security-focused RL environments, targeting threat vectors like Indirect Prompt Injection (IPI).
Reward Diagnostics & Trace Analysis - Analyze live loss curves and rollout traces to identify reward hacking, lazy policy convergence, and flawed or over/under-specified verifiers.
Task & Environment Auditing
Review tasks and multi-turn environments (including tool use, web navigation, and computer use) for realism, threat model accuracy, data distribution, and dataset balance.
Performance Reporting (Gym Cards)
Generate comprehensive evaluation cards detailing hill-climbing performance uplift across checkpoints, failure modes, tokens/turns per rollout, and task-level success rates.
Integration & Orchestration
Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings.

Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors. We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis. You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models. We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis. Key Responsibilities
* Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks.
* Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis.
* Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns.
* Develop ML models and predictive algorithms to automate red-teaming.
* Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers.
* Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility.
* Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry).


About Alice:
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

We are seeking a Senior AI Researcher to lead post-training evaluation, red-teaming, and reinforcement learning (RL) gym audits on open-weight models. The ideal candidate will establish rigorous benchmarking methodologies, evaluate large language models (LLMs) against complex threats like Indirect Prompt Injections (IPI), and construct post-training evaluation pipelines that accurately measure realistic frontier-level security capabilities. Key Responsibilities RL Post-Training & Benchmarking
* Execute post-training runs (e.g. GRPO) using mainstream open-weight generalist models against security-focused RL environments, targeting threat vectors like Indirect Prompt Injection (IPI).
* Reward Diagnostics & Trace Analysis - Analyze live loss curves and rollout traces to identify reward hacking, lazy policy convergence, and flawed or over/under-specified verifiers. Task & Environment Auditing
* Review tasks and multi-turn environments (including tool use, web navigation, and computer use) for realism, threat model accuracy, data distribution, and dataset balance. Performance Reporting (Gym Cards)
* Generate comprehensive evaluation cards detailing hill-climbing performance uplift across checkpoints, failure modes, tokens/turns per rollout, and task-level success rates. Integration & Orchestration
* Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings.
Hybrid:
Yes

we are a leading trust, safety, and security company. Just like Alice herself, we go down the rabbit hole into the emerging world of AI and focus on safeguarding these technologies. In a world where AI has fundamentally changed the nature of risk, our company offers coverage across the entire AI lifecycle. We're currently on the ground floor of an exciting new initiative: building an "Elite Collective" of the world's premier cyber specialists-high-stakes, project-based work for the "best of the best" red-teamers in the industry. Work is on-demand and project-dependent, scaling up and down based on the needs of the client. This is not a high-volume, repetitive role; it is an opportunity to be tapped for specific, high-impact interventions when your unique expertise is required. The most sophisticated technology challenges in the world are waiting down the rabbit hole. Were looking for the sharpest minds to help us secure the frontier. The invitation is open: Come join us at the tea party. About the position: As one of our companys Security Red Team Specialists, youll focus on Generative AI Models. You will play a critical role in enhancing the security and integrity of our cutting-edge AI technologies. Your primary responsibility will be to conduct analysis and testing of our generative AI systems, including but not limited to language models, image generation models, and any related infrastructure. Your objective is to help clients secure their AI models and frameworks by identifying weaknesses, assessing risks, and providing clear steps for improvement. Key Responsibilities
* Simulated Cyber Attacks: Conduct sophisticated and comprehensive simulated attacks on generative AI models and their operating environments to uncover vulnerabilities.
* Vulnerability Assessment: Evaluate the security posture of AI models and infrastructure, identifying weaknesses and potential threats.
* Risk Analysis: Perform thorough risk analysis to determine the impact of identified vulnerabilities and prioritize mitigation efforts.
* Mitigation Strategies: Collaborate with development and security teams to develop effective strategies to mitigate identified risks and enhance model resilience.
* Research and Innovation: Stay abreast of the latest trends and developments in AI security, ethical hacking, and cyber threats. Apply innovative testing methodologies to ensure cutting-edge security practices.
* Documentation and Reporting: Maintain detailed documentation of all red team activities, findings, and recommendations. Prepare and present reports to senior management and relevant stakeholders.
About us:
we are a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, our company provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. we are widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors.
We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis.
You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models.
We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis.
Key Responsibilities
Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks.
Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis.
Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns.
Develop ML models and predictive algorithms to automate red-teaming.
Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers.
Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility.
Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry).

As an Adversarial AI Evaluation Scientist, you will review and validate adversarial agent tasks: indirect prompt-injection (IPI), data-exfiltration, tool-misuse, and related attack scenarios to ensure each one is realistic, fair, deterministic, reproducible, and correctly graded. You will decide whether a scenario genuinely demonstrates a model vulnerability or merely an artifact of how the task was built, and you will combine adversarial-security judgment with statistical analysis and LLM-assisted tooling to find quality issues, analyze attack-success patterns, and recommend or automate improvements.
Responsibilities
Review adversarial tasks for realism and threat-model soundness: is the attackers capability plausible, can the attacker actually reach the poisoned content, and is the target system a realistic one?
Assess attack fairness and attribution: confirm the harm is driven by the adversary (not pre-baked into the system prompt, vouched for by a trusted layer, or otherwise rigged), so a careful agent could plausibly pass.
Validate grading correctness: verify that success/failure criteria, reward logic, and deterministic checks detect the intended harm, with no reward hacks, token-collapse, or evadable gates.
Measure and interpret attack success rate (ASR@K) and its variance; verify deterministic, reproducible behavior across repeated executions.
Judge attack quality - novelty vs. known patterns, sophistication, stealth, and coverage across harm types and target surfaces.
Identify anomalies and systemic issues across large sets of tasks (e.g. scenarios that succeed ~100% of the time against every model - a red flag for an unfair task, not a win).
Define validation guidelines, acceptance criteria, quality thresholds, and release gates for adversarial tasks.
Use LLMs and AI models to analyze attack failures, identify root causes, and assist with task or grader fixes.
Build automated validation, regression, and quality-monitoring pipelines for adversarial task sets.
Work closely with red-teamers, researchers, and task authors to improve attack design, fairness, and reliability.

As an Adversarial AI Evaluation Scientist, you will design, analyze, and validate adversarial agent tasks: indirect prompt-injection (IPI), data -exfiltration, tool-misuse, and related attack scenarios. Ensuring each one is realistic, fair, reproducible, and correctly graded. You will author new attack scenarios and graders from scratch, pressure- TEST and review existing ones, and decide whether a scenario genuinely demonstrates a model vulnerability or merely an artifact of how the task was built. The role combines adversarial-security creativity with statistical analysis and LLM-assisted tooling to build strong attacks, find quality issues, analyze attack-success patterns, and recommend or automate improvements. Responsibilities: Create Design and author adversarial agent tasks - IPI, data -exfiltration, tool-misuse, and related scenarios - including the threat model, attack surface, payload, and the deterministic grading logic (success/failure criteria and reward checks). Invent novel attack mechanisms and concealment/exfiltration techniques that go beyond known patterns, and build realistic target environments (e.g. email/doc assistants, tool-using agents) in RL Gym-style task formats. Write grading rubrics, verifiers, and deterministic checks that detect the intended harm without reward hacks, token-collapse, or evadable gates.
?Analyze Measure and interpret attack success rate (ASR@K) and its variance; verify deterministic, reproducible behavior across repeated executions. Identify anomalies and systemic issues across large task sets. Use LLMs and AI models to analyze attack failures, identify root causes, and assist with task or grader fixes. Judge attack quality: novelty vs. known patterns, sophistication, stealth, and coverage across harm types and target surfaces.
?Review & Validate Review adversarial tasks for realism and threat-model soundness: is the attackers capability plausible, can the attacker actually reach the poisoned content, and is the target system realistic? Assess attack fairness and attribution: confirm the harm is driven by the adversary, so a careful agent could plausibly pass. Validate grading correctness: verify that success/failure criteria, reward logic, and deterministic checks detect the intended harm, with no reward hacks, token-collapse, or evadable gates.
Scale & Collaborate Define authoring and validation guidelines, acceptance criteria, quality thresholds, and release gates for adversarial tasks. Build automated validation, regression, and quality-monitoring pipelines for adversarial task sets. Work closely with red-teamers, researchers, and task authors to improve attack design, fairness, and reliability.

About us:
We are a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, we provide end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative and driven to secure the future of AI, we want to hear from you!

We're looking for a hands-on Senior Backend Engineer to join the App Studio & Intelligence team within GenAI Group. Our group builds the company's core Generative AI products - including a company-wide agents platform and customer-facing AI assistants - enabling teams across and our customers to design and deploy AI-driven solutions.
In this role, you'll design and build scalable microservices and cloud-native infrastructure at the heart of our GenAI applications, working with advanced AI capabilities such as knowledge bases, embeddings, LLM orchestration, agent frameworks, and evaluation systems. You'll be a senior technical voice on the team - owning complex features end-to-end, raising the engineering bar, and shaping how AI capabilities are delivered in production.
What You'll Do:
Design, build, and own backend services and infrastructure for our GenAI applications - scalable, resilient, production-grade systems running on AWS.
Build the APIs, data pipelines, and backend integrations that power advanced AI capabilities, including knowledge bases, embeddings, LLM orchestration, agent workflows, and evaluation frameworks, with attention to performance, scalability, and security.
Lead complex design efforts - driving architecture discussions, design reviews, and technical decisions for significant areas of the system.
Collaborate closely with product, data science, and other engineering teams to translate business needs into robust backend solutions that enable AI-agent use cases.
Raise the engineering bar through code reviews, mentoring less experienced engineers, and championing best practices in quality, observability, and operational excellence.
Take ownership of production health, including monitoring, alerting, troubleshooting, and continuously improving system reliability.