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We are looking for a hands-on Senior AI Engineer with real-world experience building and deploying LLM-based and Agentic Systems to production.

You will be responsible for taking AI solutions from concept to reliable, scalable production systems, working across AI, backend, cloud, and infrastructure.

Responsibilities
Design, develop, and deploy LLM-based and Agentic Systems for production environments.
Take AI/Agentic solutions from PoC and concept stages to stable, scalable, and reliable production systems.
Develop backend services and AI solutions using Python.
Work hands-on with agentic frameworks such as LangGraph, CrewAI, or similar/custom frameworks.
Deploy and operate AI systems in cloud environments.
Troubleshoot complex system issues and improve observability, reliability, and performance.
Collaborate with development, DevOps, security, and other technical teams.

We're looking for an exceptional AI Architect / Tech Lead (Hands-on) to help shape the future of our platform.
‍About the Role:
This is a highly technical, hands-on leadership role for someone who loves solving complex engineering challenges.
You'll lead the architecture and development of our AI initiatives while remaining deeply involved in coding, technical decision-making, and mentoring the engineering team.
If you're passionate about building AI-native products, designing scalable systems, and leveraging the latest AI technologies to accelerate development-you'll fit right in.
What You'll Do:
Design and lead the architecture of complex AI-powered features and services.
Build scalable Full Stack solutions using Node.js and React.
Develop AI-native products using modern LLM technologies.
Drive technical decisions, architecture, and engineering best practices.
Mentor developers through hands-on technical leadership.
Work closely with Product, Design, and Customer Success to deliver impactful solutions.
Explore and adopt the latest AI tools to improve both our product and development workflows.

we are looking for a AI Engineer (Mid-Senior).
What You'll Do:
- Build and harden AI Workers for real enterprise workflows - procurement, finance, HR, sales, risk, integration.
- Own the MCP connector framework - reusable tool servers that power every AI Worker.
- Design multi-step agentic flows with planning, reflection, retries, approvals, and clean failure modes.
- Ship evaluation suites that catch regressions before customers o.
- Instrument cost, latency, and reliability; drive them down relentlessly.
- Partner with Product and Solutions to ship features that survive contact with enterprise reality.

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.

We are looking for a professional Senior AI Enablement to drive AI and business transformation initiatives, combining strategic thinking, execution discipline, and strong cross-functional stakeholder management.
This role will play a key part in shaping and driving AI-enabled transformation across the organization, working closely with business, product, AI, and IBT teams to translate strategic priorities into clear execution plans, adoption frameworks, and measurable business impact.
The role will be responsible for driving transformation execution governance, including initiative prioritization, business process understanding, dependency management, meeting outcomes, follow-up cadence, risk tracking, and cross-functional accountability across strategic initiatives.
Responsibilities:
Drive AI and business transformation initiatives from strategy into structured execution.
Work closely with business, product, AI, and IBT stakeholders to shape AI-driven capabilities and adoption processes.
Lead transformation execution governance, including planning, prioritization, follow-up, dependency management, and execution rhythm.
Translate business priorities into actionable plans, clear workstreams, and measurable outcomes.
Define KPIs, ROI models, and cost-related considerations to support decision-making and value realization.
Identify risks, gaps, dependencies, and blockers, and ensure timely escalation and resolution.
Build and implement transformation processes, governance structures, and execution frameworks.
Support senior stakeholders with clear communication, structured updates, and presentation materials.
Drive cross-functional alignment and accountability across multiple teams and initiatives.

You will be part of the Platform team, reporting to the VP of R&D. This is a hands-on IC role at the crossroads of data science, software engineering and cloud infrastructure: you design the service, you build every layer of it, you ship it, you measure what it does for the products that use it, and you maintain and extend it with the features its users need.
Responsibilities
Own services end to end: From a one-page design through API, SDK, UI and infrastructure to production, built to the platforms service standards: tested at every level, auto-deployed on merge, with live status and cost per job.
Build data services at scale: Spark and EMR jobs over the lakehouse, orchestrated in Airflow, that resolve identities, join vendor feeds and score millions of records per run, and that get faster and cheaper with every release.
Put models and LLMs into production: Foundation model, embedding, classification and agent services, each with an evaluation harness behind it, so a model change is measured before a product feels it.
Build the full stack: Backend services, web consoles and SDKs that an engineer, a product team or an agent can use self-serve, with a user guide for people and a playbook for agents.
Run what you ship: Monitoring, alerting, retries and cost per job. You are the first responder when your service is red, and you announce every release to its users with measured numbers.
Work AI-native: AI coding agents are part of how we build every day. You drive them, review their output critically, and build services that agents consume as easily as people do.

we are looking for a we are looking for a
As an AI Algorithm Engineer , youll design and implement advanced algorithmic workflows that power our data digestion pipelines and conversational AI agent experience. You will:
Architect and build workflows that turn raw, unstructured data (e.g., PDF documents and ultra high-resolution architectural drawings) into meaningful, structured context for downstream analysis.
Combine techniques from classical NLP, computer vision, unsupervised learning, and graph theory to build robust end-to-end pipelines - not just standard LLM API calls, but intelligent decomposition, structuring, and context engineering.
Develop AI agent workflows that let customers explore, query, and reason about their data naturally and reliably.
Build strong evaluation infrastructure to benchmark and continuously improve both classical algorithmic components and LLM-based workflows.
Work primarily in Python, and collaborate across systems and services in TypeScript when needed.
Move fast from prototype to production, while maintaining correctness, scalability, and measurable quality.

We are looking for a Product Manager to shape the future of business travel through Book with AI. You will own the product across Hotels, Rental Cars, and Rail, while serving as the day-to-day product lead for the team and making complex bookings feel as natural, trustworthy, and seamless as working with an expert travel agent.
We are looking for a Product Manager to own the Book with AI product end to end across Hotels, Rental Cars, and Rail. Your mission is to make booking complex business travel feel as natural as working with an expert travel agent - while making sure the agent is accurate, policy-aware, transparent, and reliable enough for real transactions.
This is a highly cross-functional, hands-on role. Youll lead day-to-day execution across the entire traveler journey - from the initial request through search, comparison, booking, post-booking support, and recovery when things go wrong, partnering closely with engineering, design, applied AI/ML, data, travel domain experts, operations, and other product teams.
Youll shape the conversational and user-interface experiences that power Book with AI, helping travelers express their needs, evaluate trade-offs, make confident decisions, and complete bookings with less effort. Youll stay close to the customer experience by reviewing real conversations, leading bug bashes, interpreting evaluation results, identifying patterns in user feedback, and ensuring the team remains focused on the highest-impact opportunities.
The ideal candidate thrives in ambiguity, learns quickly, and is energized by AI-native products. They create clarity and momentum without waiting for perfect information, roll up their sleeves, and drive work through uncertainty and chaos.
What You'll Do:
Own the vision, strategy, roadmap, and execution for Book with AI across Hotels, Rental Cars, and Rail.
Own the full Book with AI journey: intent understanding, search, ranking and recommendations, policy and personalization, comparison, contract, checkout, booking, and post-booking support.
Define a coherent cross-LOB experience for users who want to plan and book multiple parts of a trip, including clear sequencing, handoffs, and expectations.
Improve the product's key outcomes, including successful booking completion, conversion, time to book, user effort, CSAT, trust, and repeat usage.
Lead experimentation across conversational flows, recommendations, search and booking surfaces, using clear hypotheses and measurable success criteria.

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!

Were looking for a hands-on AI Solutions Manager who combines deep practical knowledge of generative AI tools with strong business understanding. This role sits at the intersection of technology and impact - identifying real organizational needs and translating them into scalable, AI-driven solutions.

This role is perfect for someone who lives and breathes AI technology, stays ahead of the rapidly evolving landscape, and knows how to turn cutting-edge capabilities into real-world solutions.

What Youll Do

Collaborate with cross-functional teams to identify opportunities where AI can solve complex business and internal problems
Evaluate and select the right AI tools and models (Claude, Gemini, OpenAI, etc.) based on use case, limitations, cost, performance, and scalability
Design and implement AI-powered solutions and internal automations using LLM APIs and workflow platforms (e.g., N8N, Workato)
Build and optimize prompts, RAG pipelines, and AI agents while understanding model limitations and trade-offs
Architect scalable AI workflows and integrations across systems (e.g., Salesforce, Jira, Confluence, , etc.)
Lead hands-on projects from ideation to rollout, iterating rapidly to deliver value
Stay up to date with emerging AI capabilities and continuously evaluate new tools and frameworks
Educate and guide the organization how to adopt and use wisely AI tools and models

We are looking for a Senior Data Science Researcher to join Honeycomb's AI team, the team behind underwriting decisions, quote-time risk scoring, and portfolio analytics.

In this role, you will own entire research directions: framing the problem, deciding what's worth measuring, designing the approach from first principles, and turning
the result into production signal. The work is production-oriented applied research on hard, real-world datasets where standard recipes don't apply and the right method
often has to be invented for the problem.

What You'll Do

- Own open-ended research directions end-to-end - from a vague business question to a deployed, validated signal.
- Work across the team's core research areas - risk modeling, environmental and catastrophe modeling, and behavioral modeling - translating complex real-world processes
into reliable predictive signal.
- Deeply investigate existing production models - understand what they actually learn, where they break, and where the headroom is; identify and ship optimizations
grounded in that understanding.
- Design approaches from first principles when off-the-shelf methods don't fit the data.
- Build rigorous evaluations and own the result in production, not just the notebook.
- Document research clearly so findings are reproducible, auditable, and compound over time.

We are seeking a AI Engineer to join our team and implement advanced AI solutions in our travel and expense management products. The ideal candidate will have strong backend engineering expertise and enthusiasm for working with cutting-edge AI technologies.
You'll be responsible for designing and implementing backend services that integrate AI capabilities including large language models (LLMs), retrieval-augmented generation (RAG), and conversational interfaces into our consumer-facing products. This is an opportunity to expand your expertise into AI while leveraging your strong engineering foundation.
What Youll Do:
Design and develop scalable, high-performance backend services and APIs.
Implement integrations with AI platforms and services.
Work with AI specialists to translate AI capabilities into production-ready features.
Ensure high reliability, performance, and security of AI-powered systems.
Collaborate with product and engineering teams to gather requirements and deliver solutions.

Were looking for a talented AI-native Software Engineer to join our innovative R&D team!
We help our customers acquire better users and spend less doing it - and our core team owns the engine behind it. We turn raw customer data into ad-network conversions, end-to-end: through large-scale ML and data pipelines, event-driven delivery services on AWS, and the UI that ties it together. There's no narrow lane here - you own problems wherever they live in the stack.
We're an AI-native team: we treat AI and agentic tooling as a first-class part of how we build, and it's how a small team credibly owns this much surface area. We're looking for a high-ownership generalist who works this way (or is hungry to), and who thrives in a correctness-critical domain where a bug means real customer ad-spend going the wrong way.
Responsibilities:
Turn raw customer data into ad-network conversions end to end, building across the whole stack: ML and data pipelines, delivery services, and the customer-facing UI.
Take features from idea to production largely on your own, designing, shipping, monitoring, and iterating, using AI and agentic tooling as a force multiplier, and owning your systems in production where mistakes translate directly into customer spend.
Collaborate closely with the team lead, product, and data scientists to take models and ideas from prototype to reliable, scaled-up production.

We are looking for a hands-on wireless systems engineer to lead the development of a multi-RAT digital twin for our Open RAN solution. The digital twin will execute production RAN software-beginning with scheduler and MAC behavior-in a closed loop with PHY, channel, UE, traffic, and network models. It will allow engineering teams to design, evaluate, and compare features for LTE, 5G NR, and 2G without requiring a dedicated physical radio setup for every development cycle.
This is a senior individual-contributor role at the intersection of wireless systems, simulation, production software, and AI/ML. You will evolve an existing LTE end-to-end simulator into a scalable engineering platform for feature development, regression testing, performance optimization, and evidence-based pre-validation. Initial use cases include MAC scheduler and link-adaptation improvements, power control, mobility and interference scenarios, and neural-network-assisted channel estimation.
The successful candidate will understand that a useful digital twin must be both fast and trustworthy. You will define multiple fidelity levels-from rapid surrogate models to full PHY processing-and establish repeatable methods for calibrating the twin against lab or field reference data. The goal is to reduce dependence on continuous lab access while maintaining clear, measurable confidence in the simulation results.
What you will do:
Own the technical architecture and roadmap for a modular, multi-RAT RAN digital twin
covering LTE, 5G NR.
Integrate production MAC and scheduler software into deterministic, per-TTI/slot closed-loop simulations
through stable and maintainable interfaces.
Model the interaction among scheduler decisions, PHY processing, propagation channels, UE behavior, traffic, interference, mobility, HARQ, link adaptation, and power control.
Extend the current LTE simulation capability and define reusable abstractions that support additional 5G NR and
2G stacks without duplicating the platform.
Design a fidelity ladder that combines high-fidelity PHY execution with faster calibrated models or lookup/surrogate backends, selecting the least expensive model that is valid for each engineering question.
Develop and evaluate AI/ML-based RAN capabilities, including neural channel estimation, learned link adaptation or scheduling policies, and ML-based PHY or channel surrogates.
Build representative datasets and experiment pipelines; establish conventional algorithmic baselines;