We're building one of the most agentic, fast-moving AI products in the web search category - and we need a leader who can own it end to end. This is a cross-functional group leader role sitting at the intersection of AI product leadership and high-scale AI infrastructure: you'll lead the team building our agentic search product, and own the data infrastructure that indexes and caches the web at scale.

What Youll be Doing:
Set technical and product direction for an agentic web search AI system, from research and benchmarking through production and scale.
Bring deep, hands-on expertise in LLMs, agentic architectures, evaluation/benchmarking, and modern AI/LLMOps practices; make build-vs-buy and architecture calls with authority, not just strategy.
Lead and develop small group of two teams with top notch engineers, providing technical leadership, growing engineering managers, and driving execution across both product and infrastructure.
Drive the product to a category-winning position within an aggressive timeline.
Own the infrastructure supporting an API surface currently serving millions of calls/month, with a mandate to lead its growth to billions/month.
Bring proven, hands-on experience scaling high-throughput, high-availability infrastructure - not just AI-adjacent platform experience - including reliability, cost, and performance at that scale.
Own the data infrastructure needed to run the product, including large-scale data pipelines, storage, and operations.
Lead a worldwide web caching and indexing operation at high scale, ensuring freshness, coverage, and cost-efficiency of the index.

We need a Director to set the strategy for everything underneath it - cloud, security, and the delivery lifecycle - and to bring AI into how that infrastructure is run.

This is a director-level leadership role, not an IC stretch assignment. You will lead our DevOps organization - which already has a team leader running day-to-day delivery - and operate one level up: setting direction, owning the roadmap, making architecture and build-vs-buy calls with authority, and representing infrastructure at the leadership table. Close enough to the work to guide it in depth, senior enough to own it end to end.

What Youll be Doing:
Own cloud infrastructure and security as one strategy: architecture, scale, and the path to our next order of magnitude of growth, with our security and compliance posture (IAM, secrets, hardened baselines, SOC 2 / ISO 27001 audit readiness) built in from the start, not bolted on.
Own the SDLC end to end, and drive AI into every stage of it: measure delivery performance (lead time, deployment frequency, change failure rate) and use AI to improve each stage of the lifecycle - planning, code review, testing, release, and operations - alongside AIOps practices like automated/self-healing recovery and intelligent alerting.
Own FinOps as a strategic discipline: the cloud bill is a P&L line you're accountable for, with unit economics driven down every quarter.
Own vendor and contractual relationships across cloud, tooling, and infrastructure services - negotiating leverage, not just approving invoices.
Lead the DevOps organization through its team leader: set technical direction and standards, develop the team leader into a stronger manager, and grow the bench beneath them.
Partner as a peer with Backend, Infra, AI, Data, Product, Security and Finance leadership - infrastructure decisions get made with you in the room, not after.

We're looking for a Senior Team Lead to own two things at the core of our product: the data-pipeline infrastructure that ingests and processes web data at scale, and the agentic AI system that generates Extraction Templates - the mappings that turn any webpage into structured data.

You'll lead a team of 6 engineers spanning Backend, Full-Stack, and AI - coaching, managing, and hiring across both disciplines while staying hands-on, not just directing from a distance. Exceptional internal leadership and people skills are a must: clear communication and real ownership of team health, not just delivery.

What Youll be Doing:

Data Pipelines at Scale
Own end-to-end architecture and delivery of our data-pipeline infrastructure - from system design to large-scale data flow, storage, and operations - already running in production for Fortune 500 customers.
Continuously analyze and improve the efficiency, scalability, cost, and stability of pipeline components as volume grows by an order of magnitude or more.
Design and implement supporting interfaces (Skills, Plugins, MCP, UI, API) and platform engineering where needed to deliver pipeline capabilities end-to-end.
Partner closely with the Product and Delivery team to define and prioritize the right pipeline solutions.

Agentic Generation of Extraction Templates
Bring deep, hands-on expertise in LLMs, agentic architectures, evaluation/benchmarking, and modern AI/LLMOps practices; make build-vs-buy and architecture calls with authority.
Grow the team's AI capability day to day: actively promote and mentor AI-native development practices.
Continuously improve the Extraction Template generation capability's coverage, accuracy, and scale.

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.

We're looking for a Head of Research to establish and lead our research group - a strategic and hands-on leadership role for someone who thrives at the intersection of AI, infrastructure, and systems innovation. You'll identify high-impact research directions, validate cutting-edge technologies, and drive new approaches that translate into long-term competitive advantage for us. This is not an academic position, but an opportunity to build a research function that directly shapes product and technology.

Define and lead the company's research agenda in AI, infrastructure, and distributed systems.
Explore and evaluate new algorithms, architectures, and approaches relevant to large-scale AI workloads and cloud cost efficiency.
Collaborate with product and engineering to translate research into real-world impact.
Build and manage a small, high-caliber research team.
Publish findings, represent us in the AI/infrastructure research community, and establish partnerships with academia and industry.
Act as a thought leader driving innovation across the company.

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.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
Your Impact & Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

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 Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless company ronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We are seeking a Head of Research to build and lead our research group reporting to the Co-founder and CTO. You'll set the research agenda for how AI agents, spanning coding agents, SaaS AI platforms, cloud, and endpoints, can be abused, and you'll turn that research into the detections and runtime protections that define our product. You'll build the team, the threat intelligence function, and the processes that keep us ahead of a threat landscape that changes weekly. This is a hands-on leadership role. You'll still go deep on platforms and attack paths while scaling research into a core differentiator of the company. You will help define the field of Agentic Security and be one of its leading voices.

Responsibilities

Lead and grow the research team: Hire, mentor, and manage AI security researchers. Build a culture of rigor, speed, and ownership.

Own the research direction: Proactively identify emerging agentic threats and platforms, prioritize research projects, and set a roadmap aligned with product and customer needs.

Build threat intelligence processes: Establish how we collects, triages, and operationalizes threat intelligence on agentic attacks. This includes in-the-wild abuse, malicious MCP servers, skills and extensions, and new techniques from the research community.

Drive research into product: Partner closely with engineering and product so findings become production detections, runtime policies, and governance capabilities. Own the coverage and quality of our detection logic.

Define detection strategy: Set standards for detection logic across environments, including coverage models, efficacy measurement, and false positive and false negative management at enterprise scale.

Stay hands-on: Lead deep dives into platform architectures, monitoring and enforcement boundaries, and end-to-end PoCs for the most critical attack paths.

Represent us externally: Publish research, advisories, and vulnerability disclosures. Speak at leading security conferences and build our reputation as the authority on agentic security.

Support customers and the field: Provide research expertise for customer engagements, incident investigations, and strategic accounts.