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our company builds the production-grade AI backbone for top-tier enterprises. We don't just wrap LLMs. Our platform autonomously handles mission-critical workflows, replacing operational friction with seamless, predictive experiences. We are a lean, engineering-driven company working directly with the biggest players in the market.
An FDE at our company is a customer-embedded engineer who owns the technical success of an AI deployment end to end. You don't advise and hand implementation to someone else. You are the implementation owner. You take an ambiguous business problem, break it into solvable technical components, build the solution on our platform, and stay with it until it works reliably in production.
What You'll Do
Own deployments end-to-end. From signed deal to production go-live and stabilization. No hand-offs.
Build production AI agents. Design and ship agent flows on our platform: prompt engineering, orchestration, edge cases, and the testing discipline that makes them production-grade.
Integrate into brownfield reality. Legacy systems, APIs, CRMs, telephony and customer infrastructure, while navigating security, compliance and incomplete requirements.
Debug in environments you don't control. When production misbehaves inside the customer's stack, you find it and fix it.
Push back with judgment. Challenge poor requirements, find creative alternatives, and align technical and business stakeholders behind them.
Be embedded with customers. On-site several days a week during active deployments, often as the most senior technical person in the room. On-site days carry dedicated compensation on top of your salary, and every production go-live carries a milestone bonus.

we are searching for the best Data Scientist out there!
We are a new startup founded by successful serial entrepreneurs with the vision to evolve digital marketing from the measurement of traffic to measuring VALUE.
Advertising for the past 100+ years has faced a strange dilemma: Half the budget I spend on Advertising is wasted. The problem is - I dont know which half (John Wanamaker, 1838-1922)
We are a deep machine learning and AI company, using the latest technologies, to solve a really simple question - which half is wasted?
We are a new startup, launched in summer of 2020, backed by strategic investors from around the world who believe in what were doing.
We have customers and a constant flow of interest from the press, investors and more large companies who want to join us.
Role Description:
As a Staff Data Scientist your role will be to:
Use analytical, statistical and technical skills to understand large, complex datasets with the aim to eliminate waste and measure incrementality for advertisers.
Lead, Develop & improve algorithms for automated decision making around lifetime value, causality, multi-touch attribution, and media mix optimization
Determine, analyze, maintain, and report on core marketing metrics to identify causal relationships between marketing actions and outcomes.
Translate data into actions and recommendations - appropriately interpreting and building on findings, and fully exploiting insights
You will also participate in important decisions around the product, our strategy as well as have the opportunity to choose the areas you work on based on what you want or like to do the most. We believe in helping each other get most of ourselves rather than do what were not interested in doing.

Were hiring a Senior AI Analyst to join our Research team and help shape our AI-driven cybersecurity platform. In this role, youll dig deep into how our AI agents perform, uncover whats working and where the opportunities are, and turn that analysis into insights that shape the roadmap, strengthen the product, and drive customer value and growth.

What you'll be doing
Multi-cloud architecture: Own the design and implementation of our company's multi-cloud architecture across GCP and AWS, including the cross-cloud standards, boundaries, and tradeoffs that keep two providers from becoming twice the complexity.
Infrastructure as code: Build and maintain the entire environment in Terraform, so infrastructure is reviewable, reproducible, and scales without a person in the loop.
Deployment pipelines from scratch: Create the CI/CD foundation that lets a small team ship quickly and safely, with the testing, gating, and rollback paths that make fast releases boring rather than risky.
Kubernetes at data scale: Run and scale the Kubernetes and containerized workloads behind our company's security data processing, where volume, latency, and cost pressure all show up at once.
Security of our own stack: Own identity, access, secrets, network boundaries, and infrastructure hardening to a standard that holds up under customer security review.
Reliability and visibility: Build the logging, monitoring, and alerting layer that tells us something is wrong before a customer does, and make on-call sustainable as the system grows.
Technical direction: Partner directly with the founding engineering team on architectural decisions, and raise the infrastructure and cloud fluency of every engineer who joins after you.

Were looking for a Security Researcher to join our Research team and drive cybersecurity research at the intersection of Product Security and AI. You'll investigate security risks in modern software development, including AI-assisted and agentic workflows, and translate research into product capabilities, customer guidance, and industry insights.

our company sells a sophisticated analytics platform that combines marketing science, causal inference, and machine learning. Prospects often ask complex questions about measurement methodology, modeling assumptions, validation processes, and how results should be interpreted.
The Sales Engineer will play a critical role in the sales process by translating the technical depth of the platform into clear, compelling explanations for marketing leaders, analysts, and data teams. You will partner closely with the sales team during the sales cycle, support product demonstrations, answer technical questions from prospects, and help build trust in the methodology behind the platform.
This role sits at the intersection of sales, product, and data science.
As a Sales Engineer your role will be:
Join sales calls and product demonstrations to explain the platforms methodology and analytical approach to help overcome technical objections
Answer technical and methodological questions from prospects during the sales process.
Help prospects understand how our company differs from traditional attribution, MMM, or experimentation approaches.
Support the sales team in moving deals forward by building trust in the platforms measurement framework.
Deliver clear and engaging product demonstrations tailored to different audiences (CMOs, growth teams, data scientists, analysts).
Translate complex modeling concepts into intuitive explanations that marketers can understand.
Customize demo narratives based on the prospects marketing stack and challenges.
Support proof-of-concept discussions and pilot programs
Explain the underlying modeling logic and how the system measures incrementality.
Walk prospects through validation methods, case studies, and experimental comparisons used by customers.
Help prospects interpret platform outputs and understand how to operationalize insights.
Work closely with product and data science teams to stay current on platform capabilities.
You will also participate in important decisions around the product, our strategy as well as have the opportunity to choose the areas you work on based on what you want or like to do the most.
We believe in helping each other get most of ourselves rather than do what were not interested in doing.

Key job responsibilities

We are looking for exceptional student engineer with a passion to learn how the Cloud is working "behind the curtain".

You will learn about new features of the server, develop automation to qualify them and implement the tests into a Continuous-Integration (CI) environment that run in lab and in the actual AWS Cloud.

In this highly technical position, the Python Software Engineer student will learn scripting abilities in python to write tests to constantly check the functionality of the Graviton servers' software.
Experience coding in python and testing embedded systems while being able to leverage AI capabilities are key to success.

*Make sure to include a grade sheet with your CV, in a single PDF*

we are looking for a Customer Integration Engineer to own the full integration lifecycle for our clients - from onboarding through to ongoing support - and to help establish and enrich our integration capabilities with media vendors and various platforms.
As a Customer Integration Engineer, your role will be:
Own the full integration life cycle from definition to deployment, serving as the primary point of contact for clients during the integration phase.
Client facing, responsible for orchestrating the integration process of every new client - translating customers business objectives into technical requirements and executions.
Serve as a resource for clients, guiding them in navigating complex marketing to ensure a successful and speedy data and analytics integration.
Monitor, work to improve, and ensure that clients integration and data pipeline are functioning well.
Collaborate with clients to understand their technical needs and provide tailored solutions.
Troubleshoot, scope, and debug integration issues - handling complex technical problems through collaboration across teams.
Monitor changes on partner platforms (e.g., new API versions, breaking changes) and coordinate accordingly.
Gather feedback from clients and internal teams to continuously refine the technical side of the onboarding experience.
Identify and implement tools to facilitate easier integration and onboarding for clients.
Liaise between clients and internal teams to communicate client needs and expectations effectively.
Participate in cross-functional meetings to provide insights and updates on client integration progress.
Create specification documents and improve integration documentation, including platform tours, instructional explanations, and potentially videos.
Initiate and execute automation planning.

Were looking for a Field Security Architect to serve as a trusted advisor to our customers, helping them adopt our company and build secure-by-design practices across their SDLC.
This role sits at the intersection of Application Security, Customer Success and Advisory, and Product, with a clear focus on driving measurable customer value and security outcomes.
What youll do
Own the post-sale journey (onboarding → adoption → maturity) and drive retention & expansion
Act as a trusted advisor on secure design, threat modeling, and SDLC integration
Lead adoption of our company and translate security needs into actionable workflows
Deliver onboarding, workshops, and architecture/design sessions
Partner with Product, R&D, and Sales to drive customer impact and feedback
Proactively manage customer risks, challenges, and escalations
Drive measurable security outcomes and help customers mature their security programs.

We are looking for a Principal Engineer who lives at the frontier of AI,
someone who can envision and build autonomous agents that reason about complex infrastructure, make intelligent decisions, and execute large-scale migrations with minimal human intervention. This is a rare opportunity to define the architecture of an AI-native platform from the ground up.

Key job responsibilities

Define and architect the technical vision for an Agentic AI migration platform, where autonomous agents discover, plan, and execute full data center exits.

Design and build multi-agent systems that leverage foundation models, chain-of-thought reasoning, and tool-use patterns to solve complex migration challenges.

Architect AI-powered network transformation capabilities, using generative models to analyze, replicate, and optimize enterprise network topologies.

Provide technical leadership across 4 scrum teams, instilling an AI-first engineering culture and ensuring architectural coherence across agent frameworks.

Drive innovation in agentic orchestration to continuously improve autonomy and accuracy for migration of servers, storage, networks, and applications.

Mentor senior engineers on AI-native development practices and cultivate a culture of rapid experimentation, spec-driven engineering, and high velocity releases on Brownfield projects.

As a Senior Product Manager at our company, you will sit at the intersection of data science, engineering, and product, translating complex ML and causal inference capabilities into a product our customers love. You will work hand-in-hand with our data science and the engineering teams to shape how models are designed, evaluated, and delivered into production.
Own a significant part of the product roadmap for our value measurement platform, with a focus on data science driven features (causal inference, incrementality modeling, attribution, and media mix optimization) and customer onboarding.
Partner closely with data scientists from problem framing through experimentation, model validation, and productization, youll be expected to critically read model specs, challenge assumptions, and help prioritize research directions based on customer impact
Define success metrics and evaluation frameworks for ML models alongside the DS team, balancing statistical rigor with business usability
Own the UX of data-heavy features: collaborate with designers and data scientists to turn complex model outputs into intuitive user flows, clear visualizations, and dashboards that customers can actually act on
Design and prioritize dashboards, reports, and in-product analytics, defining what metrics to surface, how to visualize uncertainty and causal results, and how to make insights discoverable without overwhelming the user
Run user research and customer interviews to validate both the analytical value and the usability of features, and onboarding integration; translate findings into concrete design and product improvements
Translate customer problems into well-scoped technical and design requirements; translate model outputs and methodology into clear value propositions for customers
Prepare the implementation of features and support engineering, design, and DS teams through design reviews, QA, and launch, including writing acceptance criteria that account for model behavior, edge cases, data quality, and UX states (empty, loading, partial data, error)
Ensure data integrity through testing, acceptance tests, and ongoing monitoring across our diverse range of data products
Work with clients and internal stakeholders to understand business goals, identify relevant KPIs, and feed those insights back into the product, modeling, and dashboarding roadmap
Oversee end-to-end data flow and engineering processes, collaborating with ML, data, and engineering teams to ensure efficient and accurate operation during critical phases like client onboarding and daily production runs
Strategically plan for missing or imperfect data, designing product behavior and UX for new client onboarding, process failures, client-specific configurations, long-term seasonality, and external signals/features
Drive experimentation: help design A/B tests, back-tests, and validation studies to assess the impact of new models, features, and UX changes.

As a SCE Deal Lead, you will be responsible for managing the end to end Deal Cycle for strategic, large, complex or highly competitive deals. You will focus on earning trust with customers by creating actionable strategies, developing and shaping opportunities, and leading deal engagements through negotiations and closure. This highly visible role will own engagement with customer C-level executives, IT teams, and multiple lines of business to achieve business outcomes, increase the adoption of AWS services, and to enable private pricing, go-to-market, and other strategic relationships. You will work collaboratively to drive results by partnering with AWS customers, AWS field sales executives, and other internal stakeholders to empower our customers to evolve, address challenges, and to create innovative solutions.

Key job responsibilities
Lead negotiations and customer closure for strategic, large, complex or highly competitive deals.
Develop and shape the overall deal strategy and structure to meet customer business outcome and goals.
Contribute to developing our value proposition and solutions
Drive revenue growth and Cloud adoption.
Closely collaborate with key stakeholders across the organization for (EMEA) regional sales teams, and related regional and global stakeholders (Service Teams, Finance, Legal, etc.).
Act as a trusted advisor in the development of the commercial strategy of deals with AWS Field Sales Executives: partner in the execution of the sales cycle for strategic, complex, or highly competitive commercial opportunities
Inspire, influence, and facilitate alignment with internal stakeholders, experts, and other resources not under direct control, to remove obstacles and achieve desired business outcomes
Develop strategies for pricing and discounts; effectively communicate and identify deal blockers
Lead or support presentation of deal proposals to Customers

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 Growth Marketing Lead who wants to build, not maintain.
Someone who sees demand generation as more than channels and metrics, and who will build our entire marketing program with AI at its core. This is not about using AI as a side tool. It is about designing how we generate demand, create content, reach buyers, and measure impact around what AI makes possible today.
You will shape how shows up, how we break through, and how we convert momentum into revenue.