We're looking for a Senior Applied Researcher and Team Lead to help shape how our conversational agents are designed, evaluated, and shipped reliably at production scale.
Key Responsibilities:
Partner directly with the Applied Research Team Lead to define and evolve team process, technical standards, and the quality bar for agent behavior.
Take real user experience and data through the full arc, from concept to a reliable agent design to a shipped, production-grade product.
Own or share ownership of the standing function of assessing AI tools, models, and providers for reliability and fit.
Raise the technical bar across a team spanning research-leaning grad students to engineers newer to AI, and turn personal expertise into shared tooling and standards the whole team can use.
Operate pragmatically inside current infrastructure constraints while flagging where structural fixes are needed.

we are looking for a Senior AI Engineer - Exploration & Prototyping.
That is this role. You take an open question, run a focused spike, and come back with numbers and a recommendation. You read the source of the frameworks you evaluate rather than trusting their marketing. You build prototypes to settle arguments.
You do not own a subsystem and you do not ship to customers, which is exactly what protects the work: exploration inside a delivery team always loses to the sprint. You sit alongside the platform, research, and forward-deployed teams, you borrow their context freely, and your output is evidence they can act on.
You will be trusted with real influence early. The recommendations you write become the architecture other people build against, so the bar is not a working demo but a defensible conclusion, including the ones that say no.
What Youll Do:
Run technical spikes that close open decisions, covering agent orchestration frameworks, real-time transport, memory protocols, agent interoperability standards, LLM selection and routing, evaluation harnesses, and the production library and stack choices underneath all of it.
Build prototypes to de-risk, standing up something real quickly, proving or disproving the thing in question, and moving on without becoming attached to the code.
Read and evaluate unfamiliar codebases, going into the source of a candidate framework to find out whether it can actually support what we need rather than what its documentation implies.
Design the measurements that make a decision defensible, building the harness, running the comparison, and reporting latency, cost, and failure behavior honestly.
Own build-versus-adopt recommendations for platform infrastructure, frameworks, and libraries, including a clear statement of what it would cost to be wrong.
Write the recommendation down. Every spike ends in a short, decisive document another engineer can act on, with the evidence, the rejected options, and the reasoning behind the call
Hand off cleanly, transferring what you learned to the team that will own the capability in production, and staying available while they pick it up.
Track the landscape across agentic infrastructure, real-time frameworks, and adjacent AI tooling, and bring forward the things that genuinely change what we can build.

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.

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.

our company's product is built around an AI agent that security analysts and detection engineers work with directly. It investigates coverage questions against live enterprise security data, authors and tests detection logic, and tunes noisy alerting.
That agent is already in production with enterprise design partners. Now we need to make it dependable and scalable enough for GA.
You will own that evolution: the agent architecture, its evaluation and quality system, and the production engineering around it. This is a hands-on senior IC role with real architectural authority - you set the technical direction and you write the code.
The agent operates inside customer security environments, where a wrong action can become a customer incident. Correctness, isolation, observability, and evaluation are not polish. They are the product.
What you'll be doing
Agent architecture: Design the evolution from today's production single-agent system to a multi-agent one: orchestration, task decomposition, runtime and framework choices, and a migration path that does not break what design partners already rely on.
Agent capability: Own the prompts, context, skills, and tool design that make the agent genuinely good at detection engineering across multiple security platforms, not just plausible-sounding.
Evaluation platform: Build the harnesses, judges, and golden datasets that turn "the agent feels better" into a number, plus the CI gates that keep regressions from shipping.
Reliability and safety: Keep long-running agentic sessions healthy in production, and build the isolation and guardrails required of an agent working inside enterprise security environments.
Production debugging: Work real failures from production traces, and turn each one into an eval case that can never regress silently.
Technical direction: Make the calls on architecture, sequencing, and quality bar and be accountable for the outcome, including raising how AI-natively the whole team builds.
Cross-team partnership: Partner with product and customer-facing teams on what the agent should do, and with platform teams on the data and integrations it depends on.

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 hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar

Forward Deployed Engineering sits at the frontier of that work. We embed with our most strategic enterprise customers, running on-prem, air-gapped, and heavily regulated environments, and own the technical arc from discovery through production.

Your day might include designing a solution architecture for a high-impact agentic use case, debugging a production routing issue deep in our codebase, deploying a new MCP server with a customer team, or aligning with a CTO on a rollout plan; all in service of outstanding outcomes and rapid time to value for the organizations we work with.

This is a founding function. The first FDEs will define how we conducts field engineering: the standards, playbooks, and technical bar. The work is hands-on, commercially scoped, and high-leverage: youll partner closely with Product, Research, Platform, Security, and GTM, and what you learn in production directly shapes what the core team ships next. If you thrive under ambiguity, make fast and sound decisions when the stakes are high, and get energy from closing the gap between what customers hit in production and what we build next, this role is for you.

In This Role You Will
Own technical delivery across multiple deployments from first prototype to stable production, including core engineering work that unblocks strategic accounts.
Lead deep technical diagnosis and remediation in our codebase and production environments, model routing and gateway behavior, large-PR scaling, multi-pass behavior, performance and reliability issues.
Build customer-specific extensions (MCP servers, agent skills, platform integrations) that deliver immediate value and become durable product capability; upstream them into the main roadmap where appropriate.
Embed closely with customer engineering teams: serve as their senior technical counterpart for hands-on debugging, architecture reviews, and joint implementation.
Scope work, sequence delivery, and remove blockers early; make deliberate trade-offs between scope, speed, and quality to protect delivery timelines.
Partner with Product, Research, and Platform to convert account learnings and eval-driven feedback into roadmap items, specs, and prioritized engineering work.
Codify working patterns into tools, playbooks, runbooks, and reproducible benchmarks that Support and account teams can operate independently and help scale the field engineering function across the organization.

Required Machine Learning Manager - Agentic Modeling
ID 30306
Role Description:
In this role, you will lead a team of Machine Learning Scientists, ML and Data Engineers dedicated to building, evolving, and scaling our agentic AI platform. This includes developing reusable agent types (classification, entity extraction, guardrails, summarization, general-purpose), agent strategies for planning and reasoning, memory capabilities, context management, and multi-agent patterns .
As a technical manager, you should be passionate about agentic AI systems, keep up to date with recent breakthroughs in LLM agents, tool use, and reasoning, define and shape the team's ML roadmap, and not be afraid to get your hands dirty with code when needed.
You are expected to be the focal point for all technical aspects, make sure your team members deliver on their tasks, and work together with product managers and mission stakeholders to define and shape the roadmap. You will work independently and be responsible for making technical decisions within your team.
When it comes to management, your expertise in handling people will motivate and inspire them to reach outstanding success! You should have experience in developing people. You will mentor and coach your team while working closely with Product Managers.
Key Job Responsibilities and Duties:
Leadership in Agentic AI Development - Build, guide, and mentor a team of ML scientists and engineers in developing agent types, strategies, SDK capabilities, and production tooling for AI agents at scale.
Platform & SDK Strategy - Define and communicate the technical vision for the agentic modeling platform, ensuring it scales across missions (Trip Planning, Search, AITP, Trip Support, Content) and enables teams to go from idea to production agent in days, not months.
Agent Strategy & Reasoning - Drive development of advanced agent planning capabilities: iterative reasoning, self-correction, multi-step logic with conditional paths, and multi-agent collaboration patterns.
Context & Memory Systems - Lead the design and implementation of smart context management and agent memory (short-term session state, long-term episodic and semantic memory) as configurable building blocks.
Evaluation & Optimization - Own the integration with evaluation frameworks and production data tooling to enable systematic agent improvement across accuracy, latency, cost, and reliability.
Developer Experience - Drive improvements to developer tooling (Claude Code integration, AI Studio), ensuring low barriers to entry and fast iteration cycles for mission teams.
Foster a culture of collaboration, innovation, and excellence within the team.
Prioritize work in collaboration with Product Managers, depending on business needs and keeping stakeholders aligned at all times.