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We are seeking a Senior Backend Engineer with deep backend engineering expertise and proficiency in one or more major programming languages (e.g., Python, Java, Go, Rust, or Kotlin), along with a strong understanding of AI models and agents. As a core member of our AI Engineering team, you will collaborate with data scientists, ML engineers, and product managers to build scalable, production-ready infrastructure and APIs that power intelligent systems.

What you'll be doing:
As a Senior Backend Engineer in the AI Engineering team, you will:
Build and maintain reliable, scalable backend services to support AI agent execution and orchestration.
Develop AI agent systems for complex operational workflows using LangChain, LangGraph, LiteLLM, and Langfuse.
Orchestrate a hybrid model stack that includes OpenAI and Google Gemini alongside self-hosted and fine-tuned LLMs like Gemma and Llama.
Build and maintain integrations with clinical systems (FHIR, EMR).
Drive observability and reliability using OpenTelemetry, Datadog, and Langfuse.
Design APIs (GraphQL, REST), background workers, and event-driven systems that interface with AI inference engines and agent runtimes.
Collaborate with Data Science, ML, and engineering teams to deploy AI features and improve the performance, scalability, and reliability of backend systems.
Participate in code reviews, knowledge sharing, and mentoring to elevate the teams technical capabilities.

we are pioneering the future of Product Security with the first AI Security Architect. As AI transforms how software gets built, were reshaping how it gets secured, shifting security to the design phase, where the biggest impact is made.
Backed by top-tier investors and already trusted by Fortune 500s, we are defining a new category and setting the standard for how AI will power the next generation of secure software development.
At our company, we are on the forefront of integrating artificial intelligence with security to empower product security teams. As an Applied AI Engineer, you will be instrumental in architecting and developing the company AI risk engine. This engine will analyze vast amounts of unstructured data from, assess risks, predict potential security issues, and formulate actionable mitigation strategies.
What will you do:
Develop and refine machine learning models and generative AI systems to automatically understand and categorize risks in Application Lifecycle Management systems
Collaborate with product and engineering teams to integrate AI tools seamlessly into existing workflows, enhancing the proactive identification of security risks
Analyze data from a variety of sources, ensuring that the AI systems adapt to new patterns and insights dynamically
Participate in the ongoing development of our company's AI capabilities, pushing the boundaries of automated security risk assessments
Communicate complex AI concepts and the benefits of proposed solutions to non-technical stakeholders.

We are seeking a visionary and hands-on Staff AI Scientist to help shape the next generation of foundational language models and agentic systems that empower small and medium-sized businesses to thrive. In this role you will define, design, and build groundbreaking AI solutions that understand, reason, and act across complex financial and business contexts.
You will collaborate closely with cross-functional experts across AI research, data science, engineering, and product to create intelligent agents that help SMBs automate financial tasks, make smarter decisions, and operate more efficiently. Your work will combine scientific rigor with visionary execution, balancing theoretical innovation with large-scale practical impact.
As a Staff Scientist you set technical direction beyond your own projects. You are a builder-scientist - a creative thinker who develops novel model architectures and training paradigms and also drives their implementation and validation in real-world applications - and you raise the scientific bar for the teams around you. You will help shape the AI foundation that powers transformative products serving millions of customers.
Responsibilities:
Set technical direction. Define the technical strategy and roadmap for next-generation agentic and foundation-model systems, and drive innovation and scientific excellence across the organization.
Build foundational models and agents. Design and develop foundational language models and agentic architectures that power intelligent decision-making for small and medium businesses.
Advance the science. Explore novel approaches in large-scale pretraining, fine-tuning (SFT and RLFT/GRPO), retrieval-augmented generation, and reasoning to improve factuality, adaptability, and alignment.
Solve real financial work. Build intelligent agents that understand financial data, customer context, and business workflows - from bookkeeping and autonomous accounting to planning, communication, and insight generation.
Leverage our data moat. Turn our unique ecosystem signals - counterparty behavior, two-sided transaction visibility, and business twins - into defensible modeling advantages.
Ship end-to-end. Take work from experimentation to production, owning evaluation frameworks and metrics that prove real-world impact.
Elevate others. Mentor scientists and engineers, review technical designs, and grow the craft of the wider team - a core expectation at the Staff level.
Collaborate across functions. Partner with AI research, engineering, product, and design to translate breakthrough science into scalable products used by millions.
Share knowledge. Publish and present research findings, internal innovations, or contributions to open-source frameworks where appropriate.

We're looking for an AI Builder to join our Marketing team- someone who will design, build, and ship AI-powered tools and workflows that make marketing faster, smarter, and more effective. This role reports to the VP of Marketing.
If you're a creative, self-driven builder who moves seamlessly from ideation to execution, this role was made for you. You thrive in dynamic, fast-moving environments, take full ownership of your projects, and don't need hand-holding to turn an idea into a working tool. You'll be the team's go-to AI owner - collaborating closely with marketing stakeholders while also pursuing your own ideas, embracing a fail-fast mindset to try, scale, or kill initiatives quickly.
What will you do?
Own marketing AI tools: Fully own, build, and maintain all AI-driven tools that support the marketing team.
Lead lead-generation initiatives: Drive growth-hacking and meeting-generation pipelines end to end.
Build inbound demand: create lead magnets, interactive AI-powered assets, an SEO/AEO content engine, and on-site personalization that turn visitors into pipeline.
Build outbound demand: own list building, data enrichment, intent-signal monitoring, and 1:1-at-scale outbound sequences that create net-new pipeline.
Run an AI creative pipeline: produce video, image, and copy at volume with AI tools, giving marketing a scalable creative engine.
Educate the team on AI: Teach and keep the marketing team up to date on the latest AI trends and capabilities.
Build collaborative and independent tools: Partner with the team to build impactful day-to-day tools while also pursuing independent AI initiatives.
Unblock the AI wishlist: Address the marketing team's backlog of AI ideas that they haven't had time to implement.
Experiment and iterate quickly: Apply a fail-fast approach, testing new tools and initiatives, then scaling or killing them based on results.

We're looking for a Senior AI Engineer to design, build, and ship production-grade LLM agents that reason over workforce and skills data on top of Loomra's semantic layer. You won't just prototype - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily (Teams, Slack, Copilot), and iteration in front of enterprise customers. You'll work alongside product, data, and platform engineers to turn powerful capabilities into reliable, safe, and fast product experiences.
If you've built agents that actually made it to production - and you care as much about evaluation, guardrails, and reliability as you do about capability - we want to talk to you.
Responsibilities
Design and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Implement retrieval and RAG pipelines over structured and unstructured workforce data, grounded in our knowledge graph connecting people, jobs, and skills.
Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Build evaluation harnesses, guardrails, and safety/bias checks, and work within the governance engine so agents behave reliably, respect customer policies, and produce a full audit trail.
Ship agents in a model-agnostic way across providers (Anthropic, Google, IBM watsonx) and deploy them into Teams, Slack, and Copilot.
Optimize agents for latency, cost, and reliability at enterprise scale.
Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Partner closely with product, data, and platform teams to translate customer needs into agent capabilities.

We're seeking a Staff engineer - AI Builder to lead the design and implementation of AI-native technical frameworks that redefine how large-scale systems are built, extended, and evolved.

This role is deeply technical and architecture-driven, focused on designing modular, extensible, and AI-first system foundations that enable scalable development with AI as a core engineering collaborator.

You will work across the full stack, building infrastructure where AI actively generates, extends, and maintains software components as part of the native development flow.

This is not about incremental AI integration - this is about architecting the frameworks that let AI scale software engineering 10x faster and smarter.

Responsibilities
Architect and lead the development of AI-native system frameworks that emphasize modularity, extensibility, and adaptive scalability.
Build platform primitives that enable dynamic AI-driven module and extension generation.
Design and implement developer workflows optimized for AI-assisted software development, embedding AI collaboration as a first-class design principle.
Define and codify AI-native engineering practices, patterns, and guidelines to elevate our software development lifecycle.
Establish and champion best practices for AI-assisted engineering across system design, code quality, testing, observability, and operational scalability.
Drive hands-on prototyping and iterative delivery across frontend, backend, and orchestration layers.
Provide technical leadership and mentorship, fostering an AI-first engineering culture across teams.
Partner closely with Product, AI, and Engineering teams to align system evolution with AI-native goals.

We're looking for a Data Science Tech Lead to take technical and delivery ownership of this team, a hands-on senior IC role. You'll set research direction, own client-facing delivery end-to-end, and drive how the team builds and scales its use of GenAI tooling and agentic capabilities. You'll work closely with other data scientists & analysts and partner daily with Solutions, Implementation, and Engineering. This role comes with real latitude to shape research direction and influence our product offering and roadmap - not just execute against a backlog.

What you will do
1. Research Direction

Set the technical research agenda for our tabular prediction models.
Guide how we incorporate temporal signal - case history, lag features, schedule snapshots- without defaulting to time-series techniques that don't fit our data.
Own our strategy for extracting value from unstructured data sources via LLMs - feature extraction, embeddings, or fine-tuning - balancing accuracy, cost, and constraints like latency and privacy.
Set standards for experimentation, evaluation, and model guardrails.
2. Client Project Delivery

Own DS delivery end-to-end across concurrent client engagements - from discovery through modeling, validation, and go-live.
Partner with Solutions and Implementation on discovery and rollout; represent Data Science in client meetings, presenting results directly to stakeholders.
Ensure delivery lands inside the product, on timeline, coordinating with Engineering on productionization.
Track delivery risk across the client portfolio, flagging blockers and scope changes early.
3. GenAI Tooling & Team Capability

Build the team's onboarding and delivery playbook for new use-cases and encode it as reusable Claude skills/agents
Turn repeatable steps in the DS workflow into standardized, shared tooling.
Raise the team's GenAI fluency through mentoring on Claude Code and related tooling.

Were looking for a Technical Integration Manager to join our team and help connect the dots between our business goals and R&D efforts. This hybrid role is ideal for someone who enjoys working between business and technology, combining project coordination with technical understanding.
Youll be the main connection between our business units and R&D - understanding business needs, translating them into clear requirements, aligning priorities, and helping teams deliver successfully. Youll also support product and project activities to move initiatives forward.

Responsibilities

Serve as the main point of contact between business stakeholders and R&D
Translate business needs into clear requirements and technical briefs
Work closely with R&D to ensure alignment on scope, timelines, and delivery expectations
Own development initiatives end-to-end spec, tasks, QA, rollout, and monitoring whether you build them yourself using AI tools, automation and no-code, or hand them to R&D developers.
Write technical specs and acceptance criteria, and manage the work in task management tools.
Guide and train the business departments on new features documentation, walkthroughs, and ongoing support
Coordinate cross-functional meetings, updates, and ongoing communication
Assist in planning and prioritizing roadmaps and project timelines
Track and report on progress, risks, and blockers across multiple initiatives
Solve complex tech problems and support the business departments
Build new dashboards for internal and external users using tools like Google Data Studio, Redash, Tableau and others.

We are looking for a Principal AI Security Researcher with deep, hands-on expertise in AI red teaming and AI security to lead and scale RLGyms AI security research efforts.
This is not a traditional cybersecurity leadership role. We need someone who has directly worked on breaking, attacking, testing, and securing AI systems, particularly large language models (LLMs), generative AI applications, and agentic AI systems.
The ideal candidate has significant practical experience designing and executing AI red-team programs, developing adversarial attacks, identifying AI-specific vulnerabilities, building automated red-teaming capabilities, and translating findings into effective guardrails and security protections.
What you'll be doing:
Lead and scale multidisciplinary teams focused on AI red teaming, adversarial testing, and AI security research.
Design and execute sophisticated attacks against LLMs, GenAI applications, and agentic AI systems.
Research attack techniques including prompt injection, jailbreaks, indirect prompt injection, tool abuse, agent manipulation, data leakage, model misuse, and adversarial behavior.
Build and evolve AI red-teaming engines and automated adversarial testing systems, including RLGym.
Building and scaling global AI security teams (red teaming, adversarial research, guardrails engineering) while fostering an innovation-driven security culture.
Overseeing advanced adversarial evaluations for GenAI models, agentic AI, and multi-agent (A2A) systems
Defining and implementing AI red-teaming frameworks aligned with OWASP AI Security guidelines, MITRE ATLAS, and NIST AI RMF, and operationalizing automated red-team engines to continuously stress-test models at scale.
Partnering with product and engineering to design and deploy enterprise-ready AI guardrails - including policy enforcement layers, monitoring pipelines, and anomaly detection systems - and championing secure deployment practices for GenAI (including agent orchestration via MCP and A2A workflows).

The Director of AI Enablement is both a builder and a transformation leader, someone who can identify opportunities, design and implement AI solutions, and drive adoption across the organization to create measurable business value.

Reporting directly to the CEO, this is a highly visible, hands-on leadership role at the intersection of business transformation, technology, and organizational change.

The successful candidate will lead AI roadmap, build practical AI solutions, drive adoption across functions, and help transform how work is performed throughout the organization.

What Will You Do?

Define and execute AI roadmap.

Identify, prioritize, and implement high-impact AI opportunities across the business.

Partner with business, operational, engineering, and corporate functions to understand workflows, pain -points and improvements opportunities.

Translate business challenges into practical AI use cases and measurable outcomes.

Design, build, pilot, and scale AI-powered solutions, including AI agents, copilots, automations, knowledge assistants, and business applications.

Partner with IT, Engineering, Infrastructure, and Security teams to ensure scalable, secure, and sustainable solutions.

Lead the transformation, not just the technology; drive AI adoption across departments and help leaders redesign processes and ways of working to maximize AI value.

Work hand-in-hand with HR and business leaders to lead the organizational transformation required to successfully adopt AI, build internal capabilities, and embed new ways of working across the company.

Establish and lead the core pillars of the AI Enablement program: Adoption, Infrastructure, and Solution Development.

Build and lead a community of AI Builders and Leaders across the organization.

Establish AI governance, Responsible AI practices, and security standards.

Measure and communicate business impact through adoption, productivity gains, efficiency improvements, time savings, and ROI.

Success is measured not only by building AI solutions, but by driving meaningful improvements in productivity, efficiency, quality, decision-making, and business performance across the organization.

Were looking for an AI Software Engineer to own the RL Gym platform end-to-end: from architecting multi-site web environments that simulate real-world attack surfaces, to optimizing our in-house orchestration harness (AgenticVerse) for high-performance delivery into customer training pipelines.
This is a builder role. Youll lead a small team (including a dedicated web environments engineer), operating with high autonomy, moving fast from concept to working prototype to production system. Youll interact directly with customer engineering teams to understand their infrastructure constraints and deliver environments that meet their scale and reliability requirements.
Why this role
This is one of the few roles in the industry where your code directly influences how the next generation of AI models are trained. Youll be at the center of advancing AI safety, building systems that the worlds top labs depend on to make their models more robust. The work is technically deep, the problem space is genuinely novel, and the field is moving faster than any team can keep up with alone. Theres no playbook. Youll write it.
What youll do:
Platform & performance
Own and evolve AgenticVerse, our in-house orchestration harness that provisions and manages RL environments at scale. Focus on performance: low-latency provisioning, high concurrency, minimal overhead per environment instance
Design and build isolated, reproducible web environments using Firecracker microVMs or Docker containers
Architect multi-site scenarios (3-4 interconnected web applications per task) with rich interactions: drag-and-drop, file uploads, authentication flows, LLM-in-the-loop components
Implement deterministic verifiers that evaluate agent behavior with zero ambiguity
Customer delivery
Work directly with engineering teams at leading AI labs to integrate RL Gym environments into their training and evaluation pipelines
Translate customer specs into working environments, iterating rapidly on feedback
Own the technical relationship: SLAs, API contracts, integration architecture
Adapt environment delivery formats to cus tomer infrastructure (real-time API calls vs. offline batch, managed vs. raw artifacts)
Build customer-facing UIs when needed (dashboards, environment configuration portals, monitoring interfaces)
Rapid prototyping
Take ambiguous problem descriptions and produce working prototypes within days, not weeks
Validate new environment types, interaction patterns, and verifier approaches quickly
Build internal tooling that accelerates scenario authoring and testing.

We are looking for a highly technical Senior Product Manager with deep expertise in data platforms and AI/ML to join our Product team. This is a unique opportunity for a data and AI expert to take full ownership of a strategic product domain, shaping how we leverage data intelligence and AI-driven capabilities to deliver smarter, faster security validation.
Roles & Responsibilities:
Lead product management activities throughout the entire product lifecycle in a fast-paced startup environment.
Drive the Data & AI product strategy, including defining AI model behavior/integration within cybersecurity, and leading data analytics to generate actionable product insights.
Collaborate closely with R&D, Sales, and customers to execute the roadmap, gain market insights, and drive revenue/ongoing product success.

As an AI Engineer, you will help build scalable deep learning systems for Physical AI and humanoid robotics.
You will contribute across the full AI lifecycle, from simulator integration and large-scale data pipelines to model training, evaluation, deployment, and optimization on real robotic platforms.
In this role, you will work with large-scale datasets and compute infrastructure to train state-of-the-art models that enable robots to perceive, reason, and act in the physical world.
You will help build robust, production-grade systems that bridge simulation and reality, accelerate model iteration, and bring cutting-edge AI capabilities onto real humanoid robots.

In Customer Care and Trust & Safety, real people are on the other side of every decision. You're the reason why our users feel safe using our company, and you take that seriously. From quality assurance specialists to fraud analysts, those we're looking for are stakeholder-savvy and treat every case like it's the only one that matters. So if you're methodical when the details affect peoples livelihoods, we want to hear from you.
Join our dynamic Customer Care team as a Chatbot Project Lead, taking the reins on our chatbot roadmap and stakeholder communication. This is a critical role stepping in to guide a talented team, ensuring seamless project delivery and strategic alignment as we enhance our AI-powered customer solutions.
What am I going to do?
Manage the end-to-end chatbot roadmap and sprint delivery, ensuring all project deadlines and key performance indicators are met with precision.
Translate overarching company strategic goals into actionable chatbot experience improvements, effectively communicating updates and insights to diverse stakeholders.
Mentor and provide hands-on guidance to team members, fostering their growth in areas like advanced prompting techniques on platforms such as Forethought.
Leverage data analysis to deeply understand chatbot performance, driving continuous optimization and informing strategic decision-making.
Facilitate key team meetings and foster a collaborative and inclusive team culture.

We are hiring a hands-on AI expert to shape our intelligent platform, organizing complex real estate data into actionable insights and predictive models that power smarter investment decisions.

Responsibilities

AI/ML Architecture: Design and implement scalable AI systems using models such as
LLMs (e.g., OpenAI, Anthropic) and computer vision (e.g., YOLO, CLIP)
MCP Integration: Architect and orchestrate model context flows using MCP or similar
frameworks to deliver personalized, context-aware experiences
FinTech Platform Integration: Embed intelligent features into our backend and frontend
systems, ensuring seamless UX and robust performance
Hands-On Development: Write production-grade code across Python, TypeScript, and
Node.js. Own key modules and guide technical decisions
Team Leadership: Mentor a few developers, set engineering standards, and foster a
culture of experimentation and excellence
Data Strategy: Collaborate with product and data teams to define training datasets,
feedback loops, and model evaluation pipelines