Why Cast AI?

Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production.

The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation.

Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS.

Global team, diverse perspectives
We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. 

Unicorn momentum
In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started.

Join us as we build the future of autonomous infrastructure.

This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine.

About the role

You'll join one of several teams building the low-level systems behind CAST AI's multi-cloud automation platform. The work sits at the intersection of Kubernetes, cloud infrastructure, and Linux.

Depending on your strengths, you'll work in one of these areas:

• Cluster and node lifecycle - custom node bootstrapping across AWS, GCP, and Azure. Controllers and operators managing full node lifecycle. Bridging the gap between autoscaler decisions and running VMs - including working around limitations of managed Kubernetes platforms like EKS, GKE, and AKS.
• Cross-cloud provisioning - orchestrating infrastructure across regions and clouds. Workflow orchestration, provisioning integrations, networking and identity across cloud boundaries.
• Live container migration - zero-downtime relocation of running containers using CRIU checkpoint/restore, custom CRI-O and runc forks, and forked cloud CNI plugins. Memory page transfer, TCP session migration, process-level checkpoint/restore. Kernel-level Linux engineering applied to Kubernetes.
• Storage optimization - dynamic volume rightsizing, BtrFS compression, custom CSI drivers. Full storage stack from cloud disk APIs through filesystem internals to Kubernetes PV/PVC lifecycle.

Common across all teams: Go, deep Kubernetes, multi-cloud, building beyond what the ecosystem offers.

How we build

We invest heavily in agentic development and AI-powered tooling. Engineers work with code agents and automated workflows daily. We expect you to shape how these tools evolve.

Requirements:

• Strong backend fundamentals and production-grade coding, preferably Go.
• Hands-on Kubernetes internals, cloud infrastructure, or large-scale distributed systems experience.
• Clear English communication, collaborative and self-directed.
• Bias toward action and ownership.

What would make you stand out

• Deep knowledge of EKS, GKE, or AKS internals.
• Low-level Linux experience: process internals, networking (NAT, iptables, conntrack, SDN), filesystems, storage.
• Kubernetes or cloud-native OSS contributions.
• Cloud storage depth - block storage, CSI, volume management, filesystem internals.
• Container runtime experience (CRI-O, runc, containerd) or CRIU.
• Experience with AI coding agents and agentic development workflows.
• Comfort building where there's no playbook.

Responsibilities:

• Design, build, and operate backend services, Kubernetes operators, and controllers in Go across AWS, GCP, and Azure.
• Solve infrastructure-layer problems — networking, scheduling, storage, compute, or container runtime internals.
• Build and maintain high-throughput services (gRPC/REST) and async orchestration workflows.
• Shape public and internal APIs.
• Leverage and improve AI-powered development workflows.
• Participate in design reviews, code reviews, on-call, and customer deep-dives.

What’s in it for you?

• Competitive salary (€6,500 - €9,000 gross, depending on the level of experience)
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement. 
• Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

Hiring process

• Screening call with Recruiter
• Hiring Manager interview
• Technical interview (system design)
• Live coding
• Culture Check interview with an executive

*As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr.
*Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

The role

Fraud prevention is an ever-evolving puzzle, making it one of the most rewarding and impactful areas in fintech. At GoCardless, our Fraud Prevention team develops the intelligent systems that protect our platform, our merchants, and their customers.

As a Senior Data Scientist, you will guide the design and delivery of models that operate in real time across our global payment network. You’ll work at the intersection of machine learning, graph-based detection, and behavioural modelling to stay ahead of changing patterns turning research into production systems that make a tangible difference in the fight against financial crime.

You’ll collaborate closely with Engineers, Fraud Analysts, and Product Managers to help shape the technical roadmap as we expand into new markets. Our stack is centred around Google Cloud Platform and Vertex AI, using Python, SQL, and BigQuery to build and support high-performance models at scale.

What you'll do

• Contribute to the full model lifecycle, from initial discovery and feature engineering through to production, experimentation, and continuous monitoring.
• Design and refine real-time ML systems that reduce false positives and ensure a smooth, secure experience for legitimate customers.
• Foster a responsive feedback loop, adapting models as patterns evolve to ensure our defences remain robust and effective.
• Help shape the technical direction of fraud prevention ML at GoCardless, exploring new approaches as we grow globally.
• Support the team’s growth through technical mentorship, knowledge sharing, and a commitment to high-quality code.

What excites you

• Solving complex, dynamic problems: Developing systems that can adapt and respond to changing environments.
• End-to-end ownership: Seeing a project through from a conceptual business need to a live, impactful solution.
• Strategic influence: Contributing to the ML roadmap and helping determine what we build next, rather than just how we build it.
• Holistic Data Science: Engaging in deep-dive analysis, prototyping, and live experimentation.
• Modern Infrastructure: Building production-grade models on GCP and Vertex AI, with the flexibility to explore advanced architectures.

What excites us

• You hold a degree (or PhD) in a STEM discipline, or bring equivalent depth through commercial experience.

• You have hands-on experience with modelling approaches such as deep learning, graph-based methods, or sequence models. While fintech experience is great, we value transferable skills from fields like cybersecurity or risk modelling.
• You have a track record of deploying models in production that create measurable value.
• You can translate complex ML concepts into clear, practical solutions for stakeholders across the business.
• You enjoy writing clean code and helping the team thrive through thoughtful reviews and shared learning.

Base salary range: €64,000 - €96,000

Base salary ranges are based on role, job level, location, and market data.  Please note that whilst we strive to offer competitive compensation, our approach is to pay between the minimum and the mid-point of the pay range until performance can be assessed in role. Offers will take into account level of experience, interview assessment, budgets and parity between you and fellow employees at GoCardless doing similar work.

The role

Data sits at the core of our mission. We leverage bank account data to deliver high-value, intelligent payment solutions for our customers, from enhancing payment success rates to driving payer fraud prevention.

As a Senior Data Scientist within our Payment Intelligence team, you’ll partner with Software Engineers, Product Managers, and Designers to turn big ideas into reality. You’ll own the full lifecycle of our algorithms, shaping everything from the initial concept to production-ready code that powers our global payment network.

At GoCardless, our stack is centered around Google Cloud Platform and Vertex AI, providing a high-performance environment for innovation. Our Data Scientists operate at the intersection of Python, SQL, and BigQuery to build and deploy high-performance models at scale.

What you’ll do

• Lead the end-to-end delivery of models at scale, from initial discovery and feature engineering to production, A/B testing and continuous monitoring.
• Collaborate with product and engineering peers to turn complex data into real-time, mission-critical fraud prevention solutions.
• Raise the team’s collective bar through hands-on technical leadership and knowledge sharing.
• Proactively research and integrate latest developments in ML and payer fraud prevention to drive innovation at GoCardless.

What excites you

• Being a self-starter who thrives on taking a vague business problem and owning the journey from the first prototype to a live, measurable solution.
• An opportunity to introduce and scale advanced architectures to solve high-stakes fraud and payment challenges.
• Moving beyond execution to help define the technical roadmap for Payer Fraud Prevention, ensuring we leverage the best tools for the job.
• Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.

What excites us

• You hold a degree (or PhD) in a STEM discipline or an equivalent commercial experience.
• You bring hands-on experience with sophisticated architectures, such as deep learning, graph-based, or sequence-based models (experience in Fintech, Fraud Prevention, or Payments is a big plus).
• You can translate complex ML concepts into practical product solutions and communicate these ideas clearly to non-technical peers.
• You are comfortable owning the full lifecycle, from deep-dive analysis and feature engineering to prototyping, validation, and live A/B testing.
• You lead by example, writing clean, high-quality code and raising the team’s technical bar through knowledge sharing and code best practices.

Base salary range: €64,000 - €96,000

Base salary ranges are based on role, job level, location, and market data.  Please note that whilst we strive to offer competitive compensation, our approach is to pay between the minimum and the mid-point of the pay range until performance can be assessed in role. Offers will take into account level of experience, interview assessment, budgets and parity between you and fellow employees at GoCardless doing similar work.

What is this role about?

As the Lead Machine Learning Engineer at Bondora, you will be the backbone of our Data Science delivery. Your mission is to build the robust infrastructure that powers automated model pipelines, ensures deployment reliability, and governs the full ML lifecycle from experimentation to production.

This is a strategic and hands on engineering role. You will collaborate closely with Data Science, Data Engineering, and Development teams to remove friction, improve scalability, and bring stable, high quality ML solutions into everyday decision making.

You will guide the evolution of our ML engineering stack, lead high impact initiatives, and mentor engineers while shaping a culture of technical excellence 🌿

Your main responsibilities 🎯

• Guide the technical direction of Bondora’s ML engineering stack by selecting, evaluating, and implementing technologies, tools, and processes that improve scalability and reliability.
• Lead complex, high risk, or cross departmental projects that directly influence Data Science delivery, risk model performance, and production stability.
• Act as the bridge between Data Science, Data Engineering, and Development to identify and solve systemic technical challenges.
• Design and build advanced, production grade ML infrastructure and set the engineering standard for the team.
• Ensure all ML solutions are secure, observable, resilient, and scalable, following governance, compliance, and operational best practices.
• Mentor ML Engineers through code reviews, design sessions, and hands on technical leadership.
• Identify weaknesses or inefficiencies in model or data infrastructure and drive company wide improvements.
• Represent the ML Engineering team in technical discussions and communicate architectural decisions clearly to stakeholders.

What would ensure success in this role?💪

• Strong Python engineering background and experience with MLflow, Unity Catalog, Databricks, or equivalent platforms used for model lifecycle management.
• Proven experience designing ML infrastructure, including CI/CD, containerization with Docker or Kubernetes, and orchestration with Airflow or Prefect.
• Deep understanding of monitoring and observability frameworks such as Prometheus, Grafana, or Datadog, and a track record of improving pipeline reliability.
• Expertise in model governance, versioning, auditability, and operationalization from research to production.
• Solid knowledge of cloud infrastructure on AWS, GCP, or Azure, including cost effectiveness and security best practices.
• Experience leading cross functional initiatives and mentoring engineers.
• Ability to balance innovation and stability through structured experimentation, pilot phases, and safe adoption of new technologies.

What can you expect at Bondora? 

• Contribute to Bondora’s ambitious goal of reaching 1BN in revenue. Your skills and efforts will directly impact our growth trajectory and shape the future of our company.
• Our employees deserve the best. We recognize our people with a competitive salary and a generous benefits package (5 weeks of vacation, private healthcare compensation, hobby grant, mental healthcare support, share options and much more!)
• We provide an environment that encourages your personal and professional growth. As we constantly evolve and innovate, you’ll have endless opportunities (and budget) to expand your skills and skyrocket your career.

The next steps

If you have the guts, ideas, and a team vision, apply now! 

Our hiring process: 

1️⃣ Meet Kerli H., our recruiter, for an initial conversation about your background and ambitions.
2️⃣ Complete our personality and logic assessments via Alva Labs.
3️⃣ Meet Rafael, our Head of Data Science.
4️⃣ Complete case study.
5️⃣ Chat with Juris, our Chief Credit Officer.
6️⃣ If everything aligns, welcome to Bondora 🎉

The role

Data sits at the core of our mission. We leverage bank account data to deliver high-value, intelligent payment solutions for our customers, from enhancing payment success rates to driving payer fraud prevention.

As a Data Science Manager within our Payment Intelligence team, you’ll partner with Software Engineers, Product Managers, and Designers to turn big ideas into reality. You’ll own the full lifecycle of our algorithms, shaping everything from the initial concept to production-ready code that powers our global payment network.

At GoCardless, our stack is centered around Google Cloud Platform and Vertex AI, providing a high-performance environment for innovation. Our Data Scientists operate at the intersection of Python, SQL, and BigQuery to build and deploy high-performance models at scale.

What you’ll do

• Manage and mentor a high-performing team of Data Scientists, fostering a culture of technical excellence and supporting their long-term career development.
• Oversee the end-to-end lifecycle of mission-critical ML models that power real-time payment decisions.
• Shape the strategic roadmap for the Payment Intelligence space, translating complex data challenges into actionable, high-impact goals.
• Drive cross-functional impact by working closely across disciplines to build end-to-end technical solutions, from concept to production.
• Influence Senior Leadership by acting as the bridge between technical complexity and business value, communicating ML strategy to senior stakeholders.

What excites you

• Driving cutting-edge advancements in Data, AI, and Machine Learning within the payments space with a multidisciplinary team.
• Mentoring a high-performing team and fostering a culture of technical excellence.
• Solving the complex, real-time challenges of fraud prevention and payment optimisation at scale.
• Building production-grade ML models on a streamlined GCP and Vertex AI stack to drive fintech innovation.

What excites us

• 2+ years managing Data Scientists within complex, high-stakes domains.
• A hands-on leader comfortable diving into the codebase. You bring strong expertise in Python and SQL to oversee the full lifecycle of a model, from initial prototype to robust production deployment.
• A decisive collaborator who can navigate technical trade-offs and translate complex ML concepts for cross-functional stakeholders and leadership.
• Familiarity with complex data environments and model architectures, such as deep learning (experience in Fintech, Fraud Prevention, or Payments is a big plus).
  
  

Base salary range: €64,000 - €96,000

Base salary ranges are based on role, job level, location, and market data.  Please note that whilst we strive to offer competitive compensation, our approach is to pay between the minimum and the mid-point of the pay range until performance can be assessed in role. Offers will take into account level of experience, interview assessment, budgets and parity between you and fellow employees at GoCardless doing similar work.