We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies.

You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you.

Responsibilities:
Build, train, and ship detection models end-to-end, from raw data to production.
Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results.
Partner with security researchers to turn security research outputs and domain expertise into detection capabilities.
Own evaluation: design benchmarks, build labeled datasets, and define production standards.
Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability.
Iterate fast, with a tight feedback loop between model performance and product outcomes.

Were looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
Youll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. Youll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
Develop and improve our core algorithms.
Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices-not just standalone prototypes.
Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.

we are looking for a AI Research Engineer, Security.
Our Research team operates like a startup inside : strategic, high-ownership, and working directly with leadership. We drive the innovation behind AI-native platform, researching and prototyping the next generation of capabilities and guiding both product and go-to-market teams in bringing them to customers. We work at the edge of the security industry, envisioning and building first-of-their-kind, category-defining agentic development security solutions.
What You Will Do:
Research and prototype novel approaches to application security that go beyond traditional AppSec tooling.
Build proofs-of-concept for advanced prevention, detection, triage, and remediation capabilities.
Design experiments and validate new security features through data-driven testing.
Partner with product and engineering to bring successful POCs into the core platform.
Evaluate and optimize LLM-based agents and model outputs for security use cases.

We are seeking an innovative AI Engineer to join our team in building the next generation of security protection and cyber attack detection for endpoints. In this role, you will leverage cutting-edge AI technologies and advanced agentic approaches to revolutionize how cyber threats are identified, analyzed, and mitigated. You will design, build, and optimize intelligent AI agents that analyze complex security data to deliver highly accurate, automated verdict decisions on malicious activities, ensuring endpoint resilience against sophisticated attacks.
Key Responsibilities
Agentic Framework Design: Design and implement intelligent, multi-agent AI systems capable of executing complex security analysis workflows and automated decision-making.
Context Optimization: Analyze diverse security datasets to determine the most effective context, telemetry data, and threat intelligence to feed into AI agents to maximize decision accuracy and minimize false positives.
Model Configuration & Integration: Evaluate, configure, and deploy a variety of state-of-the-art language models and AI architectures tailored for high-performance security use cases.
Data Analysis & Performance Evaluation: Perform deep data analysis on security events and model outputs to continuously refine agent logic, benchmark performance, and optimize AI-driven detection mechanisms.
Collaboration & Deployment: Partner with security research and engineering teams to integrate intelligent AI components into production systems seamlessly and securely.

As an AI Engineer at our company, you will own the intelligent decision-making pipelines that turn complex workspace telemetry into autonomous security actions. You will design, build, and deploy the autonomous reasoning workflows and advanced data classification systems that drive our company's preventive operating model. Your focus will be on creating resilient, production-grade AI systems capable of deep policy comprehension, real-time prevention at the point of adoption, and autonomous remediation of existing risks.
This is a ground-floor opportunity to shape the AI strategy of a fast-growing cybersecurity company alongside a lean, elite team of builders.
WHAT YOULL DO
End-to-End Ownership: Own our AI capabilities entirely from initial research, architectural design, and prototyping, through to production deployment, optimization, and continuous monitoring.
Design & Build Agentic Workflows: Architect multi-step AI agents capable of autonomously investigating workspace risks, interpreting complex enterprise policies, and taking precise remediation actions.
Integrate Multi-Faceted ML: Bring innovation and creative thinking to our core engine. Implement diverse ML models across our entire research and product pipeline-utilizing clustering, text extraction, document analysis, and tabular data classification.
Ship Production-Grade AI: Build high-throughput, resilient, and fault-tolerant production code. You will ensure our AI pipelines and agentic workflows are highly predictable, deeply observable, and built to scale under enterprise-grade loads.
Implement Guardrails & Evaluation: Build continuous evaluation frameworks to benchmark agent accuracy, mitigate hallucinations, and enforce strict data security/privacy guardrails.

We're looking for an AI Engineer to join our team. As an AI Engineer, youll be a key member of our AI team, designing, building, and deploying cutting-edge AI systems that leverage large language models (LLMs), unstructured data, and advanced ML techniques. Youll be instrumental in bringing AI from research to real-world production, delivering high-impact features that drive our security platform.

In this role, you'll have a unique opportunity to solve complex problems, work with large scale data and be part of the core team that shapes the future of AI in the company.


WHAT YOU WILL DO
Design and develop end-to-end AI solutions, from data ingestion and modeling to deployment and observability.
Build and fine-tune LLM-based applications to tackle complex cybersecurity challenges.
Own the full ML lifecycle - from ideation and experimentation to production readiness.
Collaborate with product, engineering, and security teams to turn AI research into user-facing features.
Work with large-scale unstructured data (e.g., logs, threat intel, alerts) to extract meaningful insights.
Evaluate and optimize AI system performance, scalability, and reliability.
Contribute to core architectural decisions related to AI and ML infrastructure.

We're looking for a hands-on Agentic AI Engineer to build practical, high-impact AI solutions for our Product Support organization.
This is not a research role. You'll design and ship real AI systems that improve support workflows and processes - building custom solutions where off-the-shelf tools fall short. The role also requires close collaboration across teams: aligning with different stakeholders' needs and priorities, and building strong, ongoing partnerships across the organization.
Responsibilities:
Partner directly with business teams to identify automation and optimization opportunities
Design and implement agent-based AI workflows to automate internal processes end-to-end
Design and build LLM-powered tools (agents, workflows, copilots) using frameworks like LangGraph / LangChain
Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
Take solutions from idea → prototype → production
Governance, Reliability & Security
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations.

A security company, is building an internal AI capability across the organization: agents that perform real tasks as the organization, with ROI and security assessed at every step. We are looking for a senior engineer who owns this end to end - a hands-on builder who also carries the senior function: setting the technical strategy, making the architecture calls, and being the person leadership trusts on internal AI.
This is not a pure IC build role and not a pure strategy role. You will personally build the flagship systems - production agents, the context layer, the evaluation and safety stack - while also deciding what gets built, in what order, and to what standard. You will work across R&D, IT, Security, BizOps, Customer, Sales, Marketing, People, and Finance, with the AI Transformation Officer owning the business portfolio.
What you'll do - strategy and senior function:
Own the technical strategy for our internal AI transformation: target architecture, model and vendor choices, build-vs-buy calls.
Shape the AI portfolio: which workflows to automate, in what sequence, with what success metrics - and push back when the wrong thing is being built.
Set and enforce the bar on ROI and security for every AI system: cost, adoption, cycle-time impact, access control, and auditability are reviewed per project, not after launch.
Act as the senior technical voice for internal AI: advise leadership, review AI initiatives across the org.
Turn one-off builds into a real internal platform: reusable patterns, shared services, and standards that AI Champions and teams adopt.
Mentor AI Champions; raise the org's practical AI level.
Manage our AI- Champions Community.
Continuously evaluate models, vendors, and frameworks, and keep us agile.
What you'll do - hands-on build:
Personally design, build, deploy, and own the flagship internal agents and AI workflows, from prototype through production.
Build and evolve our AI context layer: connectors, APIs, retrieval services, metadata, identity and authorization, controls, and tool interfaces.
Create safe action tools for agents and infrastructure for agents to work.
Build RAG, tool-calling, structured-output, and multi-step agentic workflows using the right model and architecture for each problem.
Own the production quality bar for what you ship: evaluation datasets and automated checks (accuracy, groundedness, task completion, safety, cost etc.)
Work directly with business owners to map processes, test with users, and drive each pilot to a clear scale-or-stop decision.

As a Senior Applied AI Engineer, you will lead the bridge between cutting-edge AI capabilities and production-grade software. You will design, build, and optimize the intelligent systems, agentic workflows, and LLM-driven architectures that power our companys conversational products.
Also, you will combine software engineering rigour with state-of-the-art Generative AI techniques - optimizing for conversational quality metrics, latency, and system reliability in high-scale production environments, serving millions of users. We are looking for an engineer who is hungry for AI innovation, eager to pioneer new generative capabilities, and passionate about shipping impactful AI solutions.
In this role, you will:
Design & Scale AI Systems: Architect and deploy high-performance LLM orchestration systems, agentic workflows, retrieval-augmented generation (RAG) pipelines, and AI microservices.
Bridge AI Research & Production: Transform frontier LLM research, foundation models, and generative techniques into robust, production-ready features with high availability and low latency.
LLM Evaluation & Guardrails: Design and implement automated LLM evaluation (evals) frameworks, benchmark suites, and guardrail policies to continuously measure accuracy, reduce hallucinations, and ensure safety across product flows.
Optimize Performance & Cost: Lead latency, throughput, and token-cost optimization strategies across commercial APIs and open-source models.
Set AI Engineering Standards: Elevate software craftsmanship across the team through architectural reviews, code quality, and LLMOps best practices, utilizing the latest AI tools.
Drive Product Innovation: Collaborate closely with product and engineering teams to translate complex human resource and conversational challenges into intelligent software solutions.

We are looking for an AI Engineer to build the backbone of our autonomous security platform.
our company builds AI Digital Employees who help cyber teams close the execution gap. Our first AI Digital Employee, Alex, learns, understands and takes away the burden of Identity and Access Management (IAM) tasks - proactively completing the organization's cyber objectives.
About the Role:
Youll design, build, and ship the AI systems behind Alex: production agents that reason over customer identity data, call real tools, and complete IAM tasks end to end. Starting from a cybersecurity need, youll frame the technical problem, choose an approach, and own the solution through production: architecture, implementation, evaluation, deployment, and iteration on real customer behavior.
Youll build on our companys shared agent infrastructure and extend it: new tools and integrations, workflow orchestration, context and memory management, evaluation harnesses, and the guardrails that keep an autonomous agent safe inside a customers environment.
This is a hands-on engineering role, not a research position. The goal is not to prove that an idea can work; it is to make it useful, reliable, and observable for customers in production.
What You'll Do:
Design, implement, and ship AI-powered features for complex cybersecurity workflows, from prototype to production.
Build the systems that make agents work in practice: tool integrations, planning and orchestration logic, context management, structured outputs, and recovery from failures.
Choose the right technical approach for each problem, combining models, deterministic logic, data, and conventional software where appropriate.
Define quality for each use case and build the evaluation pipelines that measure correctness, task completion, consistency, safety, latency, and cost, and wire them into CI and release.
Debug agent behavior in production: trace failures to their root cause in the prompt, tool, data, or architecture, and fix it.
Own the reliability, observability, and cost of the agent systems you build.
Collaborate with cybersecurity experts, product, and platform engineers throughout development.