SUMMARY

We're looking for a AI Research Scientist to join us in spreading the power of Wiz and push the boundaries of AI in cybersecurity. You'll be at the forefront of developing novel AI models - focusing transformers and LLMs - that advance how cybersecurity operates. Contribute to the wider AI and cyber communities through publications and conference presentations, establishing Wiz as a thought leader at the intersection of AI and cybersecurity.

WHAT YOU’LL DO

• Develop and research novel AI models, focusing on transformers and deep learning. Lead mid- to long-term projects aimed at advancing AI-driven features in the cybersecurity domain for Wiz products.
• Conduct experiments and evaluate the performance of AI models, algorithms, and techniques using real-world datasets and simulated environments.
• Stay informed about cutting-edge AI methodologies, frameworks, and tools and apply them to improve security solutions’ accuracy, efficiency, and scalability.
• Collaborate with engineering teams to design, build, and maintain production pipelines. Work closely with the Security Research and Product teams to define research goals.
• Publish research findings and AI thought leadership content and present at leading conferences such as NeurIPS, ICML, or CVPR.

WHAT YOU’LL BRING

• An M.Sc. degree in computer science, statistics, or a related field is a must; a Ph.D. degree is an advantage.
• At least 3 peer-reviewed publications in reputable academic journals or conferences.
• 5+ years of experience in leading and managing data science and machine learning projects, preferably at the intersection of AI and cybersecurity.
• Experience and hands-on familiarity with distributed cloud systems.  
• Strong knowledge of deep learning models and common model architectures such as transformer models, LLMs, CNNs, RNNs. Knowledge of programming languages that are used in AI research—like Python—and experience with AI frameworks (e.g., Hugging Face, LangChain, OpenAI, scikit-learn, TensorFlow, PyTorch).
• Proven ability to work independently in a fast-paced environment and come up with creative solutions to challenging problems.
• Excellent communication (both written and verbal) and presentation skills.

ADVANTAGE

• Knowledge of cybersecurity principles, attack vectors, and defense mechanisms.
• Experience speaking at data science and AI conferences like NeurIPS, ICML, or CVPR.

SUMMARY

We’re looking for a Data scientist Expert to join us and spread the power of Wiz. As a Data Scientist you will be responsible for driving research and development of autonomous AI agents and LLM-powered systems at Wiz. You will work closely with cross-functional teams to explore, innovate, and implement AI-driven solutions to tackle emerging threats, examine cloud security features, and enhance the company’s security posture.

WHAT YOU’LL DO

• Lead applied research on AI agents and LLM-driven features in the Wiz platform - from autonomous threat investigation agents to AI-powered security operations workflows
• Cover a range of features - from leveraging LLMs to enhance customer investigation experience to novel usage of AI for cloud security
• Collaborate with engineering teams to design, build, and maintain production pipelines
• Work closely with the Security Research and Product teams to define research goals
• Conduct experiments and evaluate the performance of AI models, algorithms, and techniques using real-world datasets and simulated environments
• Stay abreast of cutting-edge AI methodologies, frameworks, and tools and apply them to improve security solutions' accuracy, efficiency, and scalability

WHAT YOU’LL BRING

• An M.S. or Ph.D. degree in computer science, statistics, or related field OR equivalent work experience
• 5+ years of experience in leading data science and machine learning projects, with significant hands-on work building LLM-based applications or AI agents
• Deep practical experience with LLMs - prompt engineering, fine-tuning, model selection, and understanding trade-offs across providers and model families
• Experience with distributed cloud systems - hands-on familiarity with cloud-native architectures at scale
• Strong knowledge of deep learning models and common model architecture such as transformer models
• Knowledge of programming languages that are used in AI research, such as Python, and experience with AI frameworks (e.g., Hugging Face, LangChain, OpenAI, scikit-learn, TensorFlow, PyTorch)
• Ability to work independently in a fast-paced, and come up with creative solutions to challenging problems
• Excellent communication (both written and verbal) and presentation skills
• Advantage: Knowledge of cybersecurity principles, attack vectors, and defense mechanisms

Who we are

DV is the leader in digital performance solutions, helping our advertiser and agency partners verify the quality of their digital campaigns, Optimise to improve performance and Prove that they’re achieving their business outcomes, through unbiased 3rd party data and analytics. DV’s mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world’s largest brands, agencies, publishers, and digital ad platforms.  Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry. Learn more at www.doubleverify.com.

What you will do

You will lead a team of experienced Data Scientists while remaining deeply involved in the technical work.

This is a hands-on leadership role (~70% hands-on) combining direct modeling work with ownership of team direction and execution.

You will work on core systems that operate at a massive scale, where:

• Data is abundant, but labels are scarce and expensive

• problems are long-tail and ambiguous

• Systems must meet strict latency and cost constraints (pre-bid)

Your responsibilities include:

• Lead development of content classification systems across social platforms (Meta, TikTok, YouTube), web, and apps

• Design and build models across computer vision, NLP, and multimodal pipelines

• Own the full lifecycle: data selection -> labeling strategy -> training -> evaluation -> deployment

• Develop strategies for efficient data curation and labeling (active learning, auto-labeling, sampling under scale)

• Improve model quality (precision/recall) while balancing cost, latency, and scale

• Drive automation systems (auto-labeling, auto-curation, retraining loops)

• Apply modern AI approaches (LLMs, embeddings, foundation models) to real production problems

• Lead and mentor a team of senior Data Scientists, setting technical direction and pushing execution forward

• Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems

Who you are

• 3+ years of experience leading Data Science / ML teams

• 6+ years of hands-on experience in Machine Learning / Deep Learning

• Strong background in Computer Vision and/or NLP

• Experience building and deploying production ML systems at scale

• Strong understanding of real-world trade-offs (accuracy, cost, latency)

Technical requirements

• Hands-on experience with deep learning frameworks (PyTorch / TensorFlow)

• Experience with ML/DS tools (scikit-learn, OpenCV, HuggingFace, etc.)

• Experience working with large datasets and model evaluation pipelines

Advantages

• Experience with multimodal systems (vision + text + audio)

• Experience with LLMs / embeddings / foundation models

• Experience with AutoML, active learning, or data-centric AI

#Hybrid#
 

The opportunity
Join a high-impact ML team building state-of-the-art systems that optimize experiences for billions of users. You'll tackle challenging problems across diverse datasets - with the freedom to explore novel approaches and infrastructure to deploy them at scale.

What Success Looks Like:

Short-term: Ship your first production model with measurable impact within 3 months.

Long-term: Own major ML initiatives end-to-end and introduce new methodologies that expand the team's capabilities.

Your Impact:

Elevate our technical bar with fresh ML perspectives. Shape our roadmap by identifying opportunities others miss. Your research skills keep us cutting-edge while your execution speed drives results.

What You'll Gain:

Work on real-time ML systems at scale with rich, diverse data

Autonomy to define problems and drive projects from idea to impact

Freedom to explore emerging techniques and expand your scope

Fast feedback loops through strong experimentation infrastructure

What you'll be doing

• Join a high-impact ML team building state-of-the-art systems that optimize experiences for billions of users. You'll tackle challenging problems across diverse datasets - with the freedom to explore novel approaches and infrastructure to deploy them at scale.
• What Success Looks Like:
• Short-term: Ship your first production model with measurable impact within 3 months.
• Long-term: Own major ML initiatives end-to-end and introduce new methodologies that expand the team's capabilities.
• Your Impact:
• Elevate our technical bar with fresh ML perspectives. Shape our roadmap by identifying opportunities others miss. Your research skills keep us cutting-edge while your execution speed drives results.
• What You'll Gain:
• Work on real-time ML systems at scale with rich, diverse data
• Autonomy to define problems and drive projects from idea to impact
• Freedom to explore emerging techniques and expand your scope
• Fast feedback loops through strong experimentation infrastructure

What we're looking for

• Strong Deep Learning Expertise – Comprehensive knowledge of DL theory paired with the practical intuition gained from training many networks; comfortable working with SOTA pretrained models while understanding the underlying mechanics of convergence and scale.
• Proven ability to deliver production ML models with measurable business impact - from problem definition through A/B testing -> deployment and outcome tracking
• AI-Native Builder Mentality – Comfortable breaking conventional development cycles to build fast and ship often. You have a deep mastery of the modern GenAI stack for development, using it to automate the mundane and focus your cognitive load on high-level architecture and model performance.
• Strong programming skills in Python and Deep Learning stack (PyTorch, scikit-learn, XGBoost) with ability to write clean, efficient code for production systems
• Self-directed research capability - demonstrated ability to independently explore unfamiliar ML domains, synthesize technical literature, and implement novel approaches
• Structured problem-solving methodology - ability to decompose complex business problems, design solution approaches, and communicate technical findings to stakeholders

You might also have

• Positivity and enthusiasm

Additional information

• Relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

#SEN

We are looking for a highly skilled Senior Machine Learning Engineer to lead our transition from on-demand, third-party LLM APIs to a fully self-hosted, scalable model ecosystem.

Our core product is an advanced, agentic support chatbot capable of complex reasoning, API tool calling, database lookups, and orchestrating specialized Small Language Models (SLMs) for targeted NLP tasks. As we scale, our current deployment infrastructure (AWS SageMaker) is becoming unsustainable. You will be responsible for architecting, deploying, and optimizing an infrastructure capable of supporting 50 to 100 distinct models ranging from 100M to 70B parameters.

What You’ll Do:

• Inference Optimization: Deploy and manage large-scale models using high-performance inference engines (like vLLM) to ensure low latency and high throughput for our agentic chatbot.
• Agentic Workflows: Develop and refine the chatbot's agentic capabilities, ensuring reliable tool-use, routing, and interactions between massive LLMs and specialized SLMs.
• Model Fine-Tuning: Design and execute fine-tuning strategies to improve model accuracy on specific domain tasks and tool-calling execution.
• Rigorous Evaluation: Build comprehensive offline and online evaluation frameworks to constantly measure model performance and business impact through structured A/B testing.

What We’re Looking For:

Core Engineering & AI Frameworks

• Strong proficiency in Python and Bash scripting.
• Deep experience with PyTorch and the Hugging Face ecosystem.
• Experience using AI coding assistants natively in the terminal, specifically Claude Code, to accelerate development workflows.

LLMs, Inference & Agents

• Proven experience deploying models using vLLM, TGI, or similar high-performance inference servers.
• Strong fundamental understanding of LLM architectures, attention mechanisms, and generation parameters.
• Hands-on experience building Agentic systems (ReAct, function/tool calling, RAG).
• Expertise in fine-tuning strategies (e.g., SFT, RLHF, DPO) and parameter-efficient techniques (PEFT/LoRA).

Statistics & Model Evaluation

• Offline Metrics: Deep understanding of classification/summarization metrics (Precision, Recall, F1, AUC) and retrieval metrics (MRR, NDCG, Precision/Recall @ k).
• Online Metrics & A/B Testing: Strong statistical foundation to design and analyze A/B tests safely, including the use of t-tests, Mann-Whitney U tests, and bootstrapping techniques.

Bonus Points

• Containerization & Orchestration: Experience with Ray for orchestrating large-scale model deployments across multi-GPU clusters.
• Model Quantization: Experience with memory optimization techniques like AWQ, GPTQ, GGUF, or FlashAttention to fit 70B models efficiently onto hardware.
• API Development: Proficiency in building robust, asynchronous microservices using FastAPI to serve model requests.

• Knowledge of Data Engineering principles: dataset collection, cleaning, processing, and scalable storage.

Experience with core MLOps practices, including dataset versioning (e.g., DVC), experiment tracking (e.g., Weights & Biases, MLflow), and model registries.

The Data Science role at Armis is the engine behind our industry-leading Exposure Management (EM) platform.
You will be responsible for the AI models that power our CAASM (Cyber Asset Attack Surface Management) capabilities and our advanced Vulnerability Prioritization and Remediation engines.

We are building the intelligence layer that helps organizations implement a Continuous Threat Exposure Management (CTEM) framework. You will build models that normalize fragmented data from hundreds of sources, calculate complex risk scores, and prioritize which exposures actually pose a threat to the business.

We are looking for an experienced and creative data scientist, with a software development touch, to join our Data Science and Armis family. A data scientist that understands and loves data, and lots of it.

 What you will do...

• Solve Applied Product Challenges: Deep-dive into the Armis product ecosystem to identify and solve high-impact problems. You will translate complex customer needs into scalable ML features that directly improve the user experience.
• Lead Exposure Intelligence R&D: Develop features that unify asset data (CAASM) with threat intelligence. This includes building models for entity resolution (deduplicating assets across fragmented sources) and automated risk assessment.
• Advanced NLP & Knowledge Extraction: Use NLP and LLMs to parse unstructured security data—such as CVEs, threat intel feeds, and security advisories—to automate the mapping of vulnerabilities to specific business contexts.
• Predictive Prioritization: Design and optimize algorithms that go beyond static CVSS scores. You will incorporate exploitability (EPSS), reachability, and business criticality to help clients focus on the 1% of exposures that matter most.
• End-to-End Ownership: Work closely with Product Managers and Data analysts and Engineers to ensure your models aren't just accurate in a notebook, but are robust, explainable, and deliver clear value within the product UI.
• Graph-Based Attack Surface Mapping: Identify hidden patterns and relationships between assets, users, and vulnerabilities to visualize the potential "blast radius" of a security gap.

 What we expect...

• Academic Background: M.Sc./PhD in Computer Science, Statistics, Engineering, or a related field (or equivalent high-level professional experience).
• Industry Experience: 6+ years in Data Science, with a heavy focus on NLP and solving complex, real-world problems using ML/Deep Learning.
• Technical Mastery: Hands-on experience with PyTorch, Hugging Face, scikit-learn, and SQL. Familiarity with processing large-scale datasets (PySpark, or similar) is highly valued.
• Domain Awareness: Proven ability to apply statistical modeling to cybersecurity, risk management, or complex system analysis. Experience with Vulnerability Management or Graph Theory is a significant plus.
• Product and collaborative Driven Mindset: You are obsessed with understanding the "Why" behind the data. You enjoy learning the nuances of the product and the cybersecurity domain to ensure your DS solutions are highly applicable.

Curiosity & Grit: A passion for diving into "messy" data and finding the signal within the noise of the modern attack surface.

We offer the industry’s only platform that fuses customer identity and anti-fraud solutions – customer identity management, identity verification, and fraud prevention. 

We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc.

About the Role:

As the AI Backend engineer  at Transmit Security, you will join a team of highly skilled machine learning engineers in developing and deploying advanced AI/ML solutions that power our identity and security products. You’ll utilize technical skills to drive innovation, ensure delivery of high-impact projects, and scale our data-driven capabilities across the organization.

This role requires both strategic thinking and hands-on expertise. You’ll be responsible for shaping the data science roadmap, mentoring a growing team, and collaborating with product, engineering, and business stakeholders to translate business challenges into practical machine learning solutions.

What you’ll do:

• Design, develop, and maintain backend services for AI agents and tool integrations using latest technologies
• Build scalable APIs and microservices that interface with LLMs and AI frameworks
• Implement agent orchestration systems, tool calling mechanisms, and workflow engines
• Optimize performance and reliability of AI-powered applications at scale
• Develop data pipelines for training, evaluation, and monitoring of AI systems
• Integrate with various LLM providers (OpenAI, Anthropic, etc.) and manage API interactions

What you’ll need:

• Excellent coding skills in Python/TypeScript, with at least 5 years of hands-on experience building reliable backend services, agents and tooling. Familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) is a strong advantage.
• Experience designing, deploying, and maintaining production systems that integrate ML components, including APIs, microservices, model serving layers, feature pipelines, monitoring, and CI/CD/MLOps workflows.
• Solid experience with AI related contexts Understanding of prompt engineering and LLM optimization techniques, RAG architecture
• Solid understanding of distributed systems concepts, performance optimization, observability, and operating services at scale.
• Strong communication skills, with the ability to bridge technical, product, and business perspectives.
• Prior experience in cybersecurity, fraud prevention, or identity management is a plus, especially with secure system architectures or ML-augmented decisioning systems.

Advantages

• Experience integrating with LLM APIs (OpenAI, Anthropic Claude, etc.)
• Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT)
• Background in ML/AI concepts and model deployment
• Experience with message queues (RabbitMQ, Kafka) and event-driven architectures
• Experience with function calling and tool use patterns in LLMs

#LI-AM1 #LI-Hybrid 

#LI-TL1 #LI-Hybrid