דרושים » AI » Data Scientist - AI Research

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.

Position Intro:
Earnix is the first AI company purpose-built for insurance decisioning, providing the industry with the intelligence, governance, and decisioning agility to drive profitable growth, improve resilience, and operate with speed and precision in a rapidly changing risk environment. A trusted provider of production-grade AI and decisioning technology, Earnix builds on more than 25 years of experience in artificial intelligence risk, pricing, rating, analytics, and decisioning. Earnix brings vertical AI into the workflows and decisions that shape insurance performance across insurers pricing, underwriting, claims, customer engagement, retention, and other insurers high-value insurance moments. Earnixs AIOS enables insurers to orchestrate intelligence across the insurance decision lifecycle and drive governed, Real-Time decisions that deliver measurable business outcomes. Earnix is headquartered in Boston, MA and has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.

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 looking for a Senior AI Researcher to join our foundation models team and help advance large-scale models for single-cell RNA (scRNA) data.
In this role, you will focus on training and improving transformer-based and deep learning models, developing algorithmic solutions to challenging modeling problems, and adapting modern foundation model techniques to biological data. This is a hands-on research position for someone who enjoys building and training deep models, understanding their behavior, and pushing them to perform better.
You do not need prior biology experience - but you should be excited to apply your deep learning expertise to a new scientific domain.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Develop, train, and refine transformer-based and related architectures for large-scale scRNA data.
Tackle challenging modeling problems with thoughtful, principled approaches.
Apply ideas from NLP and vision (pretraining, self-supervision, transfer learning) to biological data.
Plan careful experiments, perform ablations, and deeply analyze results to guide model improvements.
Engage directly with large, noisy biological datasets and build intuition for model behavior in this setting.
Partner with engineers, data scientists, and biology experts to turn research ideas into working systems.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind Alice's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team. What your team owns The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains: 1. Content-moderation inference at scale Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models. 2. GenAI safety & red-teaming Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming Alice's leading edge into agentic-AI safety. 3. Data platform & MLOps An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection. What you'll do
* Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
* Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
* Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders. Leadership competencies
* People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
* Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
* Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
* Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.



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 an

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!

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.

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