Highlights:

Role: Senior ML-Engineer, Finance

Location: Spain, Remote

Language: Strong English required (C1)

About Us

Fundraise Up is a modern fundraising platform built to make donating to nonprofits as fast and convenient as possible. We continuously innovate to reduce page load times, boost conversion rates, and support a wide range of payment methods. Each month, people around the world contribute tens of millions of dollars through our platform.

The world’s leading nonprofit organizations trust Fundraise Up. UNICEF, the most prominent UN charity, uses our platform for 100% of its online fundraising. So does the American Heart Association, the Alzheimer’s Association, and many others. We’re proud to maintain a 4.9 out of 5 rating on leading review platforms.

We serve the enterprise segment, with a primary client base in the US, Canada, UK, and Australia.

The Team

Our product development team is currently at 150+ and growing. Team members are located across Spain, Serbia, Poland, Portugal, Turkey, Cyprus, Georgia and Armenia. We primarily communicate in Russian.

We’re a tight-knit, high-impact team where every task matters. It’s a dynamic, collaborative environment where smart, curious engineers support one another, share knowledge, and strive for excellence. We encourage open dialogue and host bi-weekly engineering meetups to explore technical topics and showcase team insights.

About the Role

We're looking for an ML Engineer with 5+ years of production experience to own a high-impact client intelligence initiative. Following a successful proof-of-concept with an external consultant, we are bringing this project fully in-house. The ultimate goal is to generate a comprehensive, enriched list of all potential clients globally — understanding their product requirements, industry verticals, and overall revenue potential — and deploy a scoring model that feeds directly into our sales pipeline.

This is an end-to-end ownership role. You will build from the ground up: data collection, enrichment, modeling, and production deployment. The project is co-managed by company executives and has a high strategic value.

What You’ll Do

• Build a market intelligence data-base via collecting different types of data (scraping, enrichment), fixing data pipeline and creating an ML model for scoring and analysis of the raw data.
• Design and operate scrapers to extract key signals from nonprofit websites, including products used, payment tools, and industry vertical indicators.
• Develop critical filters such as an "Is this website for fundraising?" binary classifier, alongside other features that distinguish high-potential prospects.
• Source and integrate financial data from international nonprofit registries, as well as third-party signals from SimilarWeb and Facebook.
• Store and structure the enriched dataset in our internal database, making it accessible and useful across the broader team for research and analysis.
• Work closely with the sales team to understand their qualification criteria. Analyze disqualified accounts in Salesforce to identify common exclusion patterns and refine scoring accordingly.
• Deploy the scoring model and own the process of integrating outputs into Salesforce in a clean, maintainable way.
• Build a scraper to monitor existing clients' websites, tracking whether Fundraise Up tools are correctly implemented across their properties.

Challenges You'll Navigate

• At the scale of ~1 million domains, expect domain duplicates, inconsistent data, and significant noise. You'll need to develop robust, cost-efficient filtering pipelines.
• A single model won't cover everything — you'll likely build several targeted sub-models tailored to specific verticals and geographies as the project matures.
• All of this needs to be accomplished without incurring high infrastructure or data costs. Pragmatic, scrappy solutions are valued here.

Requirements

• 5+ years of ML/DS experience solving real product problems
• Strong expertise in ML and mathematical statistics: solid knowledge of classical algorithms (especially gradient boosting) and understanding of modern NLP/LLM approaches
• Proven experience with large-scale web scraping and data pipeline construction
• Metrics-driven mindset: ability to connect ML metrics (ROC-AUC, F1, RMSE) with business metrics (conversion rate, LTV)
• Strong engineering culture: confident in Python with a product-oriented approach; we value clean code, knowledge of design patterns, and solid engineering practices
• Advanced SQL; ability to independently build complex datasets in ClickHouse and work with MongoDB
• MLOps understanding: hands-on experience with experiment tracking and production workflows (Docker, Git, CI/CD)
• Autonomy: ability to break down ambiguous problems, choose the right tech stack, and deliver to production

Our Tech Stack

Core: Python (uv, ruff), FastAPI, Pydantic, Docker

Models: CatBoost, Uplift Modeling (CausalML), OpenAI (RAG, Prompt-Engineering)

Data: ClickHouse, MongoDB, pandas, Polars, Redis

MLOps: MLflow, Airflow

Monitoring: Grafana, Sentry

Infra: linux server admin, distributed computation

Bonus points

• Curiosity and a hypothesis-driven mindset
• Ability to communicate complex analytical concepts to non-technical audiences
• Detail-oriented with a strong sense of ownership
• Comfort working in fast-paced, data-rich environments

Why work with us

• A strong, collaborative product team that owns what it builds
• Clear product vision and access to real customer feedback from global nonprofit leaders
• Flat structure: no politics, just great work with great people
• Transparent company culture-we share how we’re growing, where revenue comes from, and what’s next
• Long-term focus: we offer equity options and value sustained, meaningful contribution

Benefits

• Private medical insurance for the employee and their family
• 23 paid vacation days per year
• 11 paid public holidays per year
• 5 company-paid sick leave days
• English learning courses
• Relevant professional education
• Gym or swimming pool
• Home Office Setup Assistance: the company offers assistance with purchasing furniture (office chair, office desk, monitor) and other items to create a comfortable workspace
• Co-working
• Remote working

_{**Please note: All official correspondence from Fundraise Up will exclusively originate from the @fundraiseup.com domain. Exercise caution and ensure the authenticity of emails claiming to be from our company.}

Highlights:

Role: Senior ML-Engineer

Location: Spain, Remote

Language: Russian-speaking team; Strong English required (B2)

About Us

Fundraise Up is a modern fundraising platform built to make donating to nonprofits as fast and convenient as possible. We continuously innovate to reduce page load times, boost conversion rates, and support a wide range of payment methods. Each month, people around the world contribute tens of millions of dollars through our platform.

The world’s leading nonprofit organizations trust Fundraise Up. UNICEF, the most prominent UN charity, uses our platform for 100% of its online fundraising. So does the American Heart Association, the Alzheimer’s Association, and many others. We’re proud to maintain a 4.9 out of 5 rating on leading review platforms.

We serve the enterprise segment, with a primary client base in the US, Canada, UK, and Australia.

The Team

Our product development team is currently at 150+ and growing. Team members are located across Spain, Serbia, Poland, Portugal, Turkey, Cyprus, Georgia and Armenia. We primarily communicate in Russian.

We’re a tight-knit, high-impact team where every task matters. It’s a dynamic, collaborative environment where smart, curious engineers support one another, share knowledge, and strive for excellence. We encourage open dialogue and host bi-weekly engineering meetups to explore technical topics and showcase team insights.

About the Role

We're looking for an ML Engineer with 5+ years of production experience to strengthen our ML team. We operate as an internal service and centre of excellence for 10+ product teams at Fundraise Up. This means you won't be tied to a single feature: one day you might be optimising donation amounts and upsell offers, and the next you could be building a smart assistant for the admin panel or working on transaction classification.

We actively use not only classical ML, but also RL, and we're expanding our LLM-based solutions — prompt engineering and pipeline design with LLM APIs (OpenAI and equivalents). That's why we're looking for someone with a broad mindset who isn't afraid to experiment and can choose the most effective approach for each task.

The project's main audience and business team are based in the US. Although the product development team is Russian-speaking, you may occasionally need to write in English.

What You’ll Do

• Develop and deploy ML solutions across different business areas using tabular data (uplift models, recommender systems). We are also actively developing projects that will involve e-commerce mechanics.
• Select the most appropriate ML/LLM approaches or propose alternative solutions.
• Build end-to-end ML solutions: data preparation, training, API development, and monitoring.
• Design prompts and LLM API-based pipelines for specific product tasks: classification, content generation, and response quality evaluation.

Requirements

• 5+ years of ML/DS experience solving real product problems
• Strong expertise in ML and mathematical statistics: solid knowledge of classical algorithms (especially gradient boosting); NLP knowledge is a plus
• Metrics-driven mindset: ability to connect ML metrics (ROC-AUC, F1, RMSE) with business metrics (CR, LTV)
• Strong engineering culture: confident in Python with a product-oriented approach to development; we value clean code, knowledge of design patterns, and solid engineering practices
• Data skills: advanced SQL; ability to independently and efficiently build complex datasets in ClickHouse and work with MongoDB
• MLOps understanding: hands-on experience with experiment tracking tools and understanding of production workflows (Docker, Git, CI/CD)
• Autonomy: ability to break down problems, choose the right tech stack (or justify a non-ML solution), and deliver to production

Our Tech Stack

Core: Python (uv, ruff), FastAPI, Pydantic, Docker

Models: CatBoost, Uplift Modeling (CausalML), OpenAI (RAG, Prompt-Engineering)

Data: ClickHouse, MongoDB, pandas, Polars, Redis

MLOps: MLflow, Airflow

Monitoring: Grafana, Sentry

Why work with us

• A strong, collaborative product team that owns what it builds
• Clear product vision and access to real customer feedback from global nonprofit leaders
• Flat structure: no politics, just great work with great people
• Transparent company culture-we share how we’re growing, where revenue comes from, and what’s next
• Long-term focus: we offer equity options and value sustained, meaningful contribution

Benefits

• Private medical insurance for the employee and their family
• 23 paid vacation days per year
• 11 paid public holidays per year
• 5 company-paid sick leave days
• English learning courses
• Relevant professional education
• Gym or swimming pool
• Home Office Setup Assistance: the company offers assistance with purchasing furniture (office chair, office desk, monitor) and other items to create a comfortable workspace
• Co-working
• Remote working

_{**Please note: All official correspondence from Fundraise Up will exclusively originate from the @fundraiseup.com domain. Exercise caution and ensure the authenticity of emails claiming to be from our company.}

Customer experience

Customer experience at Nebius AI Cloud involves tackling customers’ challenges and directly impacting their success by solving real-world AI and ML problems at massive GPU cloud scale. You’ll not only resolve issues, but play a key role in shaping clients’ business success by optimizing their AI solutions.

Working with advanced GPUs such as H200, B200 and GB200, as well as modern ML frameworks, you’ll influence the development of the Nebius AI Cloud and gain experience at the intersection of infrastructure and AI. With minimal bureaucracy, you’ll have the freedom to innovate, take ownership and drive change. Opportunities for growth are abundant in this vibrant and supportive professional community.

The role

We are looking for a Partner Solutions Architect to serve as the technical interface between Nebius and our strategic technology partners, ranging from data platforms and MLOps vendors to AI frameworks and ISVs that build on GPU infrastructure. 

This is a hands-on engineering and integration role. You will design and develop integrated solutions, build reference implementations, enable partner engineering teams, and ensure joint customers succeed with combined offerings. You will influence Nebius’ product roadmap and drive deep technical collaboration across partner ecosystems.

You’re welcome to work from our office in Amsterdam or remotely from any EU country.

Your responsibilities will include: 

• Own the technical relationship with strategic partners as the primary interface to Nebius engineering and product
• Design and architect high-impact integration solutions, delivering clear joint customer value
• Build and maintain reference implementations and production-grade integrations
• Define enterprise-ready integration patterns (networking, security, compliance, observability)
• Translate partner and customer feedback into actionable product and roadmap requirements
• Enable partner teams through technical documentation, training, and demo environments
• Provide technical leadership in joint customer deployments, architecture reviews, and complex troubleshooting

We expect you to have: 

• 5+ years in solutions architecture, partner engineering, or technical pre-sales at cloud or data platform companies
• Strong expertise in cloud infrastructure
• Hands-on experience with GPU-accelerated AI/ML workflows (training, inference, MLOps)
• Proven background in integration architecture (API design, data pipelines, security, observability)
• Experience with Python and IaC tools
• Excellent communication skills with the ability to engage technical and non-technical stakeholders

It will be an added bonus if you have: 

• Track record of building or scaling partner technical programs or ecosystems
• Experience working with or at Snowflake, Databricks, AWS, Azure, or GCP
• Background in ML lifecycle (training pipelines, fine-tuning, inference optimization, RAG

The role

We seek an experienced Specialist Solutions Architect to support AI-focused customers leveraging Nebius services. In this role, you will be a trusted advisor, collaborating with clients to design scalable AI solutions, resolve technical challenges and manage large-scale AI deployments involving hundreds to thousands of GPUs.

You’re welcome to work on-site in Amsterdam or remotely from any other EU country.

Your responsibilities will include:

• Designing customer-centric solutions that maximize business value and align with strategic goals.
• Building and maintaining long-term relationships to foster trust and ensure customer satisfaction.
• Delivering technical presentations, producing whitepapers, creating manuals and hosting webinars for audiences with varying technical expertise.
• Collaborating with engineering and product teams to effectively prioritize and relay customer feedback.

We expect you to have:

• 3+ years of experience with cloud technologies in MLOps engineering, Machine Learning engineering or similar roles.
• Strong understanding of ML ecosystems, including models, use cases and tooling.
• Proven experience in setting up and optimizing distributed training pipelines across multi-node and multi-GPU environments.
• Hands-on knowledge of frameworks like PyTorch or JAX.
• Excellent verbal and written communication skills.

It will be an added bonus if you have:

• Expertise in deploying inference infrastructure for production workloads.
• Ability to transition ML pipelines from POC to scalable production systems.

Preferred tooling:

• Programming Languages – Python, Go, Java, C++
• Orchestration – Kubernetes (K8s), Slurm
• DevOps Tools – Git, Docker, Helm
• Infrastructure as Code (IaC) – Terraform
• ML Frameworks and Libraries – PyTorch, TensorFlow, JAX, HuggingFace, Scikit-learn

The role

We are seeking a customer-obsessed Solutions Architect to lead technical engagement with Nebius customers across Europe. This role is focused on presales excellence, strong technical storytelling, and driving adoption of Nebius AI Cloud across a wide range of industry segments.

As a Solutions Architect, you will act as a trusted technical partner to customers, helping them understand, evaluate, and adopt Nebius for their AI/ML and high-performance computing workloads. You will combine deep technical expertise with strong communication skills to clearly articulate Nebius’ value proposition and differentiate our platform in a competitive market.

You’re welcome to work remotely from Europe.

Your responsibilities will include:

• Own end-to-end technical presales engagements, from discovery through solution design, PoC execution, and production readiness
• Serve as a trusted technical advisor to customer stakeholders, including CTOs, Heads of AI/ML, and platform engineering teams
• Deliver compelling technical presentations, demos, and workshops that clearly communicate the value of Nebius across different customer segments
• Execute demos and PoCs to demonstrate performance, scalability, and cost efficiency of Nebius solutions
• Translate customer requirements into scalable, production-ready architectures and best practices
• Collaborate closely with Sales, Product, and Engineering to align customer needs with Nebius capabilities and roadmap
• Act as a technical advocate for customers internally, ensuring feedback is incorporated into product and go-to-market strategy
• Support regional go-to-market efforts, including events, meetups, webinars, and technical content creation
• Contribute to building repeatable technical playbooks and assets for the European market

We expect you to have:

• 2–5+ years of experience in technical roles such as Solutions Architect, Cloud Architect, Sales Engineer, or similar, with strong customer-facing exposure
• Proven experience in presales or customer-facing technical roles, including discovery, solution design, and PoC execution
• Strong ability to deliver technical pitches and clearly explain complex infrastructure and AI/ML concepts to both technical and business audiences
• Experience positioning cloud or infrastructure solutions and articulating value propositions in competitive environments
• Solid understanding of ML/AI workloads, including training and inference, and the underlying infrastructure requirements
• Experience with cloud platforms, Kubernetes, and distributed systems
• Hands-on experience with Infrastructure as Code (e.g., Terraform) and scripting/programming (Python preferred)
• Strong communication and storytelling skills, with the ability to adapt messaging across different customer segments
• Deep understanding of the European market, including customer expectations, buying behaviors, and local ecosystem
• Fluency in English plus another language preferred (both written and spoken), with the ability to engage confidently with local customers and stakeholders
• A highly customer-centric mindset, with the ability to balance technical depth and business value

It will be an added bonus if you have:

• Experience with GPU-accelerated workloads and AI infrastructure (NVIDIA ecosystem, CUDA, etc.)
• Hands-on experience with MLOps tools and frameworks (Kubeflow, MLflow, etc.)
• Familiarity with HPC environments and schedulers (e.g., Slurm)
• Experience contributing to regional go-to-market or developer/community initiatives

The role

We are seeking a highly skilled and customer-focused professional to join our team as a Solutions Architect specializing in Cloud infrastructure and MLOps. As a Cloud Solutions Architect, you will play a pivotal role in designing and implementing cutting-edge solutions for our clients, leveraging cloud technologies for ML/AI teams and becoming a trusted technical advisor for building their pipelines.

You’re welcome to work from France or any country in the Iberian Peninsula (Spain, Portugal, or Andorra).

Your responsibilities will include: 

• Act as a trusted advisor to our clients, providing technical expertise and guidance throughout the engagement. Conduct PoC, workshops, presentations, and training sessions to educate clients on GPU cloud technologies and best practices.
• Collaborate with clients to understand their business requirements and develop solution architecture that align with their needs: design and document Infrastructure as code solutions, documentation and technical how-tos in collaboration with support engineers and technical writers.
• Help customers to optimize pipeline performance and scalability to ensure efficient utilization of cloud resources and services powered by Nebius AI.
• Act as a single point of expertise of customer scenarios for product, technical support, marketing teams.
• Assist to Marketing department efforts during events (Hackathons, conferences, workshops, webinars, etc.)

We expect you to have: 

• 5+ years of experience as a cloud solutions architect, system/network engineer, developer or a similar technical role with a focus on cloud computing
• Strong hands-on experience with IaC and configuration management tools (preferably Terraform/Ansible), Kubernetes, skills of writing code in Python
• Solid understanding of GPU computing practices for ML training and inference workloads, GPU software stack components, including drivers, libraries (e.g. CUDA, OpenCL)
• Excellent communication skills, fluency in French or Spanish preferred
• Customer-centric mindset

It will be an added bonus if you have: 

• Hands-on experience with HPC/ML orchestration frameworks (e.g. Slurm, Kubeflow)
• Hands-on experience with deep learning frameworks (e.g. TensorFlow, PyTorch)
• Solid understanding of cloud ML tools landscape from industry leaders (NVIDIA, AWS, Azure, Google) 

About the Role

We’re hiring an AI Solutions Architect to lead the future of IT systems and automation at Fortis. This senior-level role focuses on improving how our internal tech works — using AI, automation, and innovative design to make everything more secure, scalable, and efficient.

You’ll lead the design of our IT tools and platforms, ensuring they’re modern, user-friendly, and powered by cutting-edge AI like LLMs (think ChatGPT, Gemini, and similar tools). This role reports to the Director of IT. It works closely with Security, Legal, Engineering, and other departments to ensure our IT systems are robust, safe, compliant, and built to scale.

This isn’t just an execution or admin role — it’s a leadership position for someone who can research, propose, and help drive future-focused IT strategies across the business.

What You'll Achieve

• Define and implement scalable, secure, AI-driven IT architectures aligned with business needs.
• Research, propose, and implement AI/LLM-based automation for IT processes, support systems, and knowledge management.
• Lead AI initiatives and strategy sessions, chairing technology trend discussions and quarterly IT innovation reviews.
• Architect and deploy cloud-native, automation-first IT systems — focused on resilience, performance, and zero-touch management.
• Implement AI-powered IT service desk solutions, compliance monitoring, incident response, and proactive issue resolution.
• Guide IT Operations in optimizing hardware lifecycle, SaaS platforms, and IT asset tracking through AI/automation-driven efficiency.
• Collaborate closely with Security and Legal teams to align AI and IT architecture with compliance, risk management, and privacy standards.
• Drive tool rationalization and consolidation projects using AI analytics to inform more intelligent decision-making.
• Partner with engineering, AI/ML teams, and vendors to integrate IT systems into our broader AI development ecosystem.

What You’ll Need to Be Successful

• Solid experience in IT engineering, IT architecture and cloud-native systems — with a recent, strong focus on AI, automation, and LLMs.
• Experience with AI technologies like large language models (LLMs), MLOps tools, and generative AI platforms (e.g., ChatGPT/OpenAI, Gemini AI, Notion AI). You’ve used these tools to help build or improve systems, not just test them.
• Practical experience building, integrating, or experimenting with AI models, LLMs, and AI-driven workflows.
• Working knowledge of enterprise IT environments, including Google Workspace, Okta, JAMF, Intune, Zoom, Slack, and Cisco Meraki, plus an understanding of cloud infrastructure (AWS, GCP, or Azure).
• Exceptional skills in technical documentation, architecture diagramming, and project planning and execution, plus an understanding of  GDPR, NIST, and SOC 2.
• Ability to explain complex technology and AI clearly and simply to all audiences. Skilled at leading conversations that shape the right technology direction, drive innovation, and bring new ideas to life. 
• Confident in leading executive meetings, managing AI committees, and guiding leadership teams toward smart, forward-thinking technology decisions.
• Game/software development experience strongly preferred where LLMs or automation were part of the tech stack, showcasing creativity, speed, and next-gen thinking.

We are looking for someone to join our Data department as a Data Engineer, helping us manage the integration and consolidation processes of our clients' data. In both on-premise and cloud environments, you will be responsible for designing the required information system architecture and managing and maintaining our clients' databases.

We’re looking for a proactive and analytical person who can adapt to a multidisciplinary team and is eager to help us tackle the many challenges our clients face.

What are we looking for?

Education:

• University degree in Computer Science, Software Engineering, or related fields with a technological specialization in data analysis and engineering.
• Plus: Master's or postgraduate degree in Big Data, Machine Learning, Cloud Computing, or Data Engineering.
  
  

Required Experience (demonstrable and advanced):

• Data Architecture & Systems: Extensive experience in designing, implementing, and optimizing complex, distributed information system architectures, including relational and NoSQL databases.
• Cloud Environments: Deep expertise in defining, implementing, managing, and maintaining enterprise-scale Cloud environments. Expert command of Azure and Google Cloud Platform (GCP), including PaaS/SaaS, serverless computing (Functions, Cloud Run, Azure Functions), containers (Docker, Kubernetes/AKS/GKE), and native cloud data services.
• Modern Data Warehouses: Advanced experience with cloud-native Data Warehouses like Snowflake, including dimensional data modeling, query optimization, and cost/performance management.
• Data Orchestration & Pipelines: Robust experience with data orchestration tools and building complex ETL/ELT pipelines using Pentaho, Apache Airflow, Data Factory, or similar, including CI/CD implementation for data pipelines.
• Advanced SQL: Demonstrable experience with advanced SQL, including complex analytical functions, query optimization, stored procedures, and efficient schema design.
• Software Development: Demonstrable experience with Javascript and Node.js, and Python for scripting, data automation, and ML model development.
  
  

Desired Experience:

• Customer Data Platforms (CDP): Experience in strategic definition, implementation, management, and maintenance of CDP solutions, preferably Segment, and their integration.
• Enterprise System Integration: Desirable experience with enterprise-level integration tools like Mulesoft or microservices architectures with REST/GraphQL APIs.
• Salesforce Ecosystem: Valuable experience working with Salesforce (Sales Cloud, Service Cloud, Marketing Cloud – SFMC), including data integration and automation.
  
  

Knowledge (Essential and Valuable):

• Deep knowledge and certifications in managing and optimizing cloud environments (Azure, GCP).
• Advanced knowledge and certifications in Snowflake (SnowPro Advanced/Architect is a plus).
• Proficiency in CDP tools (especially Segment).
• Knowledge of CRM / Marketing Automation tools (SFDC, SFMC, Adobe Campaign, Selligent Marketing Cloud).
• Advanced SQL knowledge (Analytical Functions, T-SQL, PL/SQL).
• Proficiency in programming languages like Javascript, Node.js, and Python.
• Strong understanding of Data Quality principles, Data Governance, and Normalization.
• Solid understanding of Machine Learning and AI algorithms for data enrichment and transformation.
• Experience with data streaming architectures (Kafka, Pub/Sub, Azure Event Hubs).
• Familiarity with MLOps and DataOps concepts.
  
  

Skills:

• Ability to analyze and solve complex problems.
• Project management and organizational skills for end-to-end data projects.
• Meticulous attention to detail and commitment to data quality.
• High capacity for discernment and prioritization in dynamic environments.
• Technical leadership and mentoring of junior profiles.
• Strategic thinking and innovation-oriented mindset.
• Excellent communication and presentation skills for clients and multidisciplinary teams.
  
  

Languages:

• Spanish (Native or Bilingual proficiency).
• English (High level C1/C2 - Essential for communication with international clients).
  
  

What would you do in this role? Here’s a list of responsibilities and tasks:

• Lead strategic requirements gathering, high-level design, and implementation of complex data solutions for client communication initiatives.
• Design and evolve scalable, efficient data architectures, selecting appropriate cloud-native technologies for each project.
• Design, develop, and optimize ETL/ELT data integration processes to ensure high-quality data ingestion, transformation, and availability.
• Implement end-to-end data solutions, ensuring validation, thorough documentation, and adherence to best practices in data engineering and security.
• Provide training and knowledge transfer to internal teams and clients.
• Maintain, evolve, and optimize existing data projects, ensuring monitoring, incident resolution, and continuous improvement.
• Manage and optimize client databases, ensuring performance, integrity, and security.
• Innovate with AI/ML: Implement data cleansing, normalization, consolidation, deduplication, and enrichment systems using ML/AI algorithms, including predictive scoring and customer clustering.
• Ensure Data Quality and Governance: Implement DQ mechanisms, guarantee data integrity, and ensure GDPR compliance across all processes.
• Strategically collaborate with clients: Participate in high-level meetings to assess current information system architectures, propose innovative improvements, and lead solution definitions for future challenges and new data platforms.
• Mentor and guide other data engineers, fostering best practices and professional development.
  
  

And since you'll give this and much more, we also want to give you much more:

• A dynamic and creative work environment.
• Important clients and international brands.
• Full-time position.
• Hybrid format.
• Christmas, Easter, July, and August: FULL REMOTE
• Flexible hours.
• Permanent contract with a six-month trial period.
• 22 working days of vacation per year + 12 extra days!
• Optional access to flexible compensation (health insurance, transportation, meals, and childcare).
• Ogilvy Wellness Program: Free psychological, legal, and financial support service. Discounts on physiotherapy and gyms.
• Ogilvy is recognized with the Family-Responsible Company (EFR) certification for its commitment to employee well-being and work-life balance.
  
  

If you’ve imagined yourself in this position, that’s a good sign. At Ogilvy, we want to work with the best talent—those who challenge us and make us better. People who are eager to experience an agency culture open to diversity as a principle and connectivity as a primary tool.

So, if you're nodding your head while reading this, send us your CV!

About Buynomics:  

Buynomics is the next-generation SaaS company in pricing and product optimization. It employs large-scale simulation technologies to digitize customers and predict their purchase behavior - in any environment. Our clients use Buynomics to make data-driven decisions about their current and new products, overall portfolio structure, pricing, promotions, trade terms, and more, 100x faster and more precisely than every other solution.

Our mission is to enable transparent, customer-centric commercial decisions. We’re building the operating system for commercial strategy, providing companies with a single source of truth for all market-facing questions related to pricing, products, and promotions.

About the role:  

Buynomics is building a modular, service-oriented platform to power advanced commercial decision-making for enterprise customers.

As we expand from multi-tenant SaaS into enterprise single-tenant deployments, we are looking for a Lead Platform Engineer to design and build our multi-cloud Kubernetes foundation.

This role focuses on:

• Secure single-tenant deployments in customer-controlled environments (Azure-first)
• Kubernetes-based service architecture across clouds
• Scalable infrastructure for distributed compute and ML workloads
• Infrastructure automation, security, and operational excellence

You will operate as a senior technical leader, defining the deployment architecture that enables our next-generation platform to run reliably across AWS, Azure, and GCP.

Why this role:

• Greenfield architecture: You are not inheriting legacy — you are designing the deployment platform from scratch
• Enterprise impact: Your work deploys into Fortune 500 customer environments across industries
• Real autonomy: You make architectural decisions, choose tooling, and own the platform roadmap
• AI-native engineering culture: We are all-in on AI-assisted development — you will work alongside a team that ships with AI daily
• Growth path: As enterprise deployments scale, this role grows into leading a dedicated platform team

What you'll do:  

• Design and implement cloud-agnostic, single-tenant deployment architecture (Azure-first, with AWS and GCP support)
• Build and maintain reusable Infrastructure-as-Code modules (Terraform)
• Define and standardize the Kubernetes platform, including workload isolation, auto-scaling, and security policies
• Implement GitOps-based CI/CD and automated environment provisioning
• Enable scalable execution of compute-heavy workloads (e.g. async job queues, ML training pipelines)
• Design secure networking patterns (private endpoints, VNet/VPC design, DNS, IAM/RBAC, zero-trust principles)
• Establish observability standards (structured logging, metrics, distributed tracing, alerting)
• Own cost governance across customer environments: cost attribution, right-sizing, and spend optimization
• Collaborate with engineering teams to define clean service deployment contracts
• Define platform standards, architectural decision records, and operational runbooks
• Ensure platform compliance with SOC 2, ISO 27001, and GDPR through automated policy-as-code guardrails
• Champion AI-assisted infrastructure workflows — we use AI coding tools across engineering and expect platform tooling to follow suit

What you'll need:

• 6+ years of experience in platform engineering, infrastructure, or DevOps/SRE roles
• Strong production experience in Azure (AKS, Azure AD, Managed Identity, Private Networking)
• Solid experience with at least one additional cloud provider (AWS preferred; GCP a plus)
• Hands-on production experience operating Kubernetes at scale (cluster lifecycle, upgrades, multi-tenancy)
• Deep expertise in Infrastructure-as-Code (Terraform), including module design, state management, and CI integration
• Strong knowledge of cloud networking (VPC/VNet, DNS, private routing, firewall configurations)
• Experience designing CI/CD and GitOps workflows
• Experience operating distributed or high-concurrency workloads
• Understanding of compliance frameworks (SOC 2, ISO 27001, GDPR) and ability to implement compliant infrastructure patterns
• Strong system design skills and ability to operate at Staff-level technical depth
• Ability to navigate ambiguity and move initiatives forward independently

Nice-to-Haves:

• Experience deploying software into customer-controlled enterprise cloud environments (single-tenant / BYOC models)
• MLOps experience (model lifecycle, training pipelines, experiment tracking with MLflow, artifact versioning)
• Databricks integration experience (workspace provisioning, Unity Catalog, job orchestration)
• GPU workload optimization and scheduling
• Experience building Internal Developer Platforms using tools like Backstage, Crossplane, or Humanitec
• Service mesh technologies (Istio, Linkerd) and zero-trust networking patterns
• Policy-as-code tooling (OPA/Gatekeeper, Kyverno)
• FinOps practices and multi-environment cost governance

What we offer:

• Dynamic Environment: Join a truly international and diverse team where your ideas matter.
• Flexible Work Model: Enjoy a remote setup with offices located in Cologne and Barcelona.
• Learning Budget: Take advantage of our employee development and education budget to enhance your skills.
• Local Language Classes: Participate in language lessons to support your integration and personal development.
• Health insurance package 
• Personal and professional growth opportunities
• Regular company events

To ensure a smooth and efficient process and that no applications are overlooked, please apply directly to the role. While we'd love to respond to everyone who reaches out, applying ensures that your application is properly tracked and considered.

Buynomics is proud to be an equal-opportunity employer. This means that we don’t discriminate based on race or ethnic origin, color, the language(s) you speak, where you (or your parents) are from, or whether or not you consider yourself to have a disability. Neither will your age, gender, gender identity, sexual orientation, religion, beliefs, or political opinions play a part in your application with us. We’re a diverse team in so many ways, and we love it that way.

#LI-Remote

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning. 

Spectrum collects data from an organization’s code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that’s extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

Spectrum is a resident of JetBrains' startup incubator, with startup speed and autonomy, and backed by 25 years of developer tooling expertise. We are looking for a top-class ML Engineer who will help us shape the future of software development. You will own our AI and ML engineering stack and help define the research agenda for our team. Your technical vision and design decisions will directly shape the product and determine its success.

Your responsibilities will include:

• Designing and building the ML/LLM solution for data ingestion, knowledge extraction, retrieval, and subsequent reasoning.
• Creating the datasets, metrics, and pipelines that drive measurable improvements across the system.
• Architecting and improving agents for context retrieval, knowledge extraction, and data alignment, which includes prompt engineering, model selection, and inference optimization.
• Establishing MLOps practices, including orchestration, observability, and experiment tracking.
• Collaborating with the engineering team on system design and with JetBrains Research on the research agenda.
• Defining hiring criteria, growing the ML team, and shaping the ML team culture.

We expect you to have:

• A proven track record as an ML/AI Lead.
• At least five years of experience in ML/AI systems, with at least two years focused on LLMs and generative AI.
• A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
• Hands-on experience with:
• Prompt engineering and LLM pipeline design, including evaluation.
• Agentic frameworks such as LangChain, LlamaIndex, LangSmith, smolagents, or an equivalent.
• Vector databases and retrieval-augmented generation (RAG) patterns.
• Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
• Strong Python skills – Kotlin knowledge would be a plus.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal. 

Our ideal candidate would have:

• Experience with ontologies, knowledge graphs, or graph-based reasoning.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• The ability to think strategically about product-led AI, beyond just training models in isolation.
• A background in code analysis, developer tools, or software engineering research.
• Experience with multi-agent systems or complex agentic workflows.
• Actively contributed to relevant open-source projects or publications.

What we offer

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.

#LI-KP1

Software engineers and AI agents alike suffer from the same problem: finding that one person or place that will answer their tough, specific question. Many solutions promise to solve this with similarity search in vector databases. Unfortunately, finding the answer is often a puzzle with pieces to be collected across a myriad of contradictory sources and cannot be solved without surgical search and careful reasoning.

Spectrum collects data from an organization's code, docs, and issues, and organizes knowledge in a unified ontology that AI agents can efficiently search through and reason over. We aim to revolutionize the semantic layer space for software-building organizations and move beyond specs that fall out of sync with code, introducing a living spec – one that's extracted from the whole system and used to keep it aligned. Spectrum is meant to be the single source of truth for all product and architectural knowledge.

A resident of JetBrains' startup incubator, Spectrum enjoys startup speed and autonomy, and is backed by 25 years of developer tooling expertise. We are looking for a Senior AI/ML Engineer to build and evolve the ML-powered systems at the heart of our product.

Your responsibilities will include:

• Designing and building the ML/LLM solution for data ingestion, knowledge extraction and retrieval, and subsequent reasoning.
• Creating datasets, metrics, and pipelines that drive measurable improvements across the system.
• Architecting and improving AI agents, which will involve prompt engineering, model selection, and inference optimization.
• Contributing to our MLOps practices, including orchestration, observability, and experiment tracking.

We expect you to have:

• At least four years of experience building ML/AI systems in production environments, with hands-on LLM experience.
• A deep understanding of the LLM ecosystem, including model architectures and fine-tuning approaches.
• Hands-on experience with:
• Prompt engineering and LLM pipeline design, including evaluation.
• Agentic frameworks such as LangGraph, Pydantic AI, or an equivalent.
• Retrieval-augmented generation (RAG) patterns and vector databases.
• Deploying and scaling LLM-powered applications using APIs (e.g. OpenAI or Anthropic) or open-source models.
• Strong Python skills – Kotlin knowledge would be a plus.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal.

Our ideal candidate would also have:

• Experience with ontologies, knowledge graphs, or graph-based reasoning.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• A background in code analysis, developer tools, or software engineering research.
• Experience with multi-agent systems or complex agentic workflows.
• Actively contributed to relevant open-source projects or publications.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• Opportunities to publish research at top venues in collaboration with JetBrains Research.

#LI-DNI

Scopely is looking for a Senior Data Scientist to join our Monopoly GO! game team in our Barcelona hub on a hybrid basis, or remotely in Spain or Portugal.

At Scopely, we care deeply about what we do and want to inspire play, every day. We are a global team of game lovers who are developing, publishing, and innovating the mobile games industry, connecting millions of people around the world daily.

What You Will Do 

As a Senior Data Scientist, you will play a key role in supporting game growth by leveraging data to build innovative, player-centric solutions. You won't just analyze data; you will own the lifecycle of data products — from identifying a player pain point to deploying a production-level solution.

• Product Vision & Strategy: Partner with Product Managers and Stakeholders to identify high-value opportunities, helping to define the data science roadmap for the one of the world’s biggest mobile games.
• End-to-End Ownership: Own the process of creating new data products, from initial inception and requirement gathering to technical design, productionalization, and monitoring.
• Player Insights: Analyze player behavior to understand how features impact the game economy, progression, social dynamics, and sentiment.
• Advanced Modeling: Design and implement A/B tests, forecasting, clustering, and ML models that don't just "predict," but provide actionable "decision intelligence."
• Technical Excellence: Provide technical expertise throughout the product life cycle, ensuring high quality, scalability, and efficiency in our Data & Insights team.
• Collaborative Delivery: Work across departments (Product Analytics, Engineering, LiveOps, Marketing) to ensure data solutions are integrated and driving real-world impact for our players.

What We’re Looking For

We want a creative, highly motivated individual who enjoys tackling ambiguous problems and "figuring it out" with practical, scalable solutions.

• Product Mindset: Outstanding problem-solving skills with a "user-first" mentality. You can conceptualize player behavior and determine the best metrics to measure success, even when data is messy or non-existent.
• Technical Stack: Advanced proficiency in Python and SQL. Experience with BI tools (Looker) and visualization libraries (Plotly, etc.).
• Data Engineering Savvy: Experience with data transformation at scale using dbt and Airflow.
• Production Experience: Proven track record of deploying data products to production (on GCS, AWS, or Azure) and implementing monitoring/alerting to ensure service quality.
• Scientific Rigor: Strong background in statistics, A/B testing methodology, and applying ML/AI to improve business understanding.
• Communication: Ability to translate complex methodologies into clear "so-whats" for non-technical stakeholders and business users.

Bonus Points

• Gaming Passion: A deep love for F2P mobile games and an understanding of game loops/economies.
• Specialized Knowledge: Familiarity with causal inference, Bayesian statistics, or MLOps platforms.
• Deep Learning & GenAI: Hands-on experience with LLMs, predictions using Transformers, or other Deep Learning architectures applied to behavioral data or content generation.

We are looking for a Senior MLOps Engineer to add to our Data team in Barcelona.

Join the User Acquisition MLOps team that builds the core ML backbone of Appodeal. We design and run the systems that power real-time predictions at massive scale, where every millisecond matters. Our work directly drives thousands of bidding operations each second, supporting a high‑throughput, low‑latency inference platform used across the company.

You will help shape and evolve a modern ML platform built around Databricks, MLflow, Unity Catalog, GitHub Actions, and deep integrations with our Predictor service. This means solving real engineering challenges: scaling training pipelines, optimizing GPU workloads, ensuring safe and automated model promotions, and keeping online inference reliable under extreme load.

You will collaborate daily with Data Science, Data Engineering, Runtime, DevOps, and Product, and you will fully own the systems and projects you touch. This is a role for engineers who like autonomy, impact, and building things that matter.

Responsibilities:

• Design, operate, and improve the ML platform, including Databricks workflows for training, MLflow and Unity Catalog for model management, CI/CD pipelines using GitHub Actions and Databricks Asset Bundles, and integration with the runtime inference layer.
• Support data scientists in developing and maintaining end-to-end training pipelines, covering data processing, feature transformations, ML model training on GPUs, and distributed training setups to ensure efficient and scalable workflows.
• Ensure reliable and safe model inference by validating models before promotion, checking metadata compatibility, monitoring latency, and supporting model evaluation flows.
• Maintain strong ML observability, including online and offline monitoring, data and feature parity checks, and automated validation of model performance in production.
• Develop internal tools and APIs that enable DS teams and account managers to experiment, validate, and promote models efficiently while reducing operational friction.

Requirements:

• 3+ years of experience in MLOps, ML Platform Engineering, or Backend roles supporting production ML systems.
• Strong Python and software engineering skills, with experience building and maintaining APIs (FastAPI, Flask, or similar).
• Hands-on experience with DNNs using PyTorch or other modern deep learning frameworks, including GPU training and optimizing hardware utilization.
• Solid understanding of distributed computing and data processing using Apache Spark or similar frameworks.
• Experience with Databricks (Jobs, MLflow, Delta, UC) or an equivalent cloud-based ML platform.
• Strong understanding of the ML lifecycle: data ingestion, experimentation, model packaging, deployment, and monitoring.
• Familiarity with real-time model inference systems and ensuring compatibility with downstream services.
• Experience with data validation libraries (Pydantic or similar) used within ML and API workflows.
• Good understanding of Docker containers, Kubernetes, and basic networking concepts.
• Experience creating and maintaining CI/CD pipelines (GitHub Actions or similar).
• Experience with orchestration tools (Databricks Jobs, Dagster, Airflow or similar).
• Demonstrated ability to use modern AI tools (Claude, ChatGPT, Gemini) to improve development speed and quality.
• Proactive mindset with a strong sense of ownership, consistently pushing work forward and anticipating next steps without needing close direction.
• Strong English communication skills.

Nice to have:

• Exposure to very high-load, low-latency ML inference environments.
• Knowledge of distributed or large-scale ML training frameworks and multi-GPU setups.
• Familiarity with Databricks Asset Bundles.
• Understanding of ONNX or other portable model serialization formats.
• Ability to work with Rust programming language.
• Familiarity with transformer architectures, encoder-decoder models, or sequence modeling approaches.
• Exposure to data engineering concepts, such as building scalable data pipelines, understanding CDC patterns, or working with structured and unstructured datasets.
• Experience with MinIO or other S3-compatible storage systems.
• Understanding of networking protocols such as TCP, HTTP, and gRPC, including their limitations in low-latency scenarios.
• Confidence navigating the Atlassian ecosystem (Jira, Confluence, Compass).

This position supposes relocation to Barcelona and work from our office in the city center.

Why Join Us:

• Opportunity to work on cutting-edge projects with a global impact in the mobile app industry.
• A collaborative and inclusive work culture that values innovation and continuous learning.
• Competitive salary, flexible work arrangements, and a comprehensive benefits package.
• Professional development opportunities and career growth prospects within a fast-growing company.

About Appodeal

Appodeal is a dynamic US-based product company with a truly global presence.

We have offices in Warsaw, Barcelona, and Parkland (FL), along with remote team members located around the world.

Our company thrives on diversity, collaboration, and innovation, making us a leader in the mobile app monetization space.

Why Appodeal?

At Appodeal, we’re more than just a company—we’re a team united by a common mission:
Help people discover and grow their talents through products that enable successful mobile app businesses.

We take pride in our cutting-edge product and our internationally distributed team of talented professionals.

Here’s what we value:

• Continuous Learning and Growth – We invest in development and long-term careers
• Making an Impact – We are building a history-defining company in the mobile industry
• Solving Exciting Challenges – Complex, large-scale problems are part of our daily work
• Enjoying the Journey – We believe great results come with a positive environment

About the Role

We are looking for a Senior Rust Engineer to join our Barcelona team and contribute to building high-performance, real-time systems powering our ML-driven AdTech platform (Mobile Growth Platform).

In this role, you will work on core backend infrastructure and ML platform components, focusing on low-latency, high-load services that process large-scale traffic in real time.

You will play a key role in developing and optimizing systems such as real-time applications, feature stores, user identity services, and custom inference layers, ensuring scalability, reliability, and performance.

Responsibilities

• Contribute to the development of real-time applications and ML platform components
• Build and maintain high-load, low-latency backend services
• Work on in-house solutions including:
  • Feature stores
  • User identity services
  • Custom inference layer
  • ML platform infrastructure
• Optimize system performance across on-premise Docker & Kubernetes environments
• Design and implement solutions for traffic scaling and system reliability
• Collaborate with ML, data, and engineering teams to support production ML systems
• Continuously improve system architecture, observability, and efficiency

Tech Stack

• Languages: Rust, Python
• Data & Streaming: Kafka, SQL, Redis, ScyllaDB, Delta Lake, Databricks
• Infrastructure: Docker, Kubernetes (on-premise)
• Tools: GitHub

Requirements

• 5+ years of experience in software engineering
• Strong proficiency in Rust
  or
  proficiency in C/C++/Zig with willingness to transition to Rust
• Experience designing and building high-load, performance-critical systems
• Solid understanding of system design, concurrency, and performance optimization
• Basic knowledge of machine learning workflows and tools

Nice to Have

• System programming experience (C, C++, Zig)
• Experience in ML engineering / MLOps
• Strong Python skills
• Familiarity with AdTech standards (OpenRTB, MRAID, VAST)
• Experience with WASM / WASI
• Background in distributed systems and real-time processing
• Location: Barcelona, Spain (office attendance 4 days per week)

What We’re Looking For

• Autonomous and self-driven engineer with strong ownership mindset
• Passion for engineering excellence, performance, and scalability
• Interest in ML/AI systems and infrastructure
• Ability to work independently and make informed decisions
• Strong collaboration and communication skills

Why Join Us:

• Opportunity to work on cutting-edge projects with a global impact in the mobile app industry.
• A collaborative and inclusive work culture that values innovation and continuous learning.
• Competitive salary, flexible work arrangements, and a comprehensive benefits package.
• Professional development opportunities and career growth prospects within a fast-growing company.

We appreciate teamwork, critical thinking, independent decision-making, and standing up for oneʼs viewpoint.
We are looking for talented engineers who are willing to grow, improve and share our values!

We are now expanding our data capabilities and seeking an experienced Head / Director of Data Science to lead and scale our Data Science UA team function in Barcelona.

This role is ideal for a leader with a strong technical background, excellent people-management skills, and the ability to turn data into actionable, high-impact product and business outcomes.

Role Overview:

The Head / Director of Data Science will be responsible for building and growing our Data Science organization, driving advanced analytics and ML initiatives, and ensuring our products and business units are empowered by high-quality insights and predictive capabilities.

You will work closely with Product, Engineering, Revenue, and Leadership teams to translate business needs into DS/ML solutions, strengthen our data infrastructure, and support decision-making across the ecosystem.

This is a strategic and hands-on leadership role with significant influence on product evolution, optimization algorithms, and operational excellence.

Key Responsibilities:

Leadership & Strategy

• Lead and scale the Data Science function across the organization.
• Translate business and product goals into clear DS/ML initiatives and execution plans.
• Build and promote a strong data-driven culture across all teams.
• Applied Data Science & ML.
• Oversee development, deployment, and monitoring of ML models across Appodeal’s product and monetization ecosystem.
• Guide experimentation, modeling, uplift measurement, and algorithmic optimization.
• Ensure model quality, reliability, fairness, and performance at scale.
• Collaboration & Stakeholder Management.
• Partner with Product and Engineering teams to integrate Data Science into product roadmaps.
• Work with business units to identify opportunities, define DS/ML requirements, and deliver impactful insights.
• Communicate DS outcomes, metrics, and recommendations to leadership.

Team Leadership

• Lead, mentor, and develop a multidisciplinary team of Data Scientists, Analysts, and ML/AI specialists.
• Foster knowledge sharing, best practices, experimentation, and continuous improvement.
• Drive hiring, talent development, and team structure optimization.

Data Infrastructure & Governance

• Collaborate with Data Engineering and MLOps teams to ensure robust pipelines, feature stores, and model deployment workflows.
• Improve data quality, observability, and governance standards.

Qualifications:

Education

Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Math, or a related field (PhD is a plus).

Experience

• 5+ years of experience in Data Science, Machine Learning, or Applied Analytics.
• 3+ years in a DS leadership role (Lead DS, Head of DS, Director of DS, or similar).
• Demonstrated experience building and leading Data Science teams.
• Proven track record of delivering DS/ML solutions with real business impact.

Technical Skills

• Strong understanding of ML algorithms, experimentation, predictive modeling, and statistical analysis.
• Hands-on experience with Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn), and analytics tooling.
• Solid understanding of data pipelines, big data technologies, and collaboration with Data Engineering teams.
• Experience with AdTech, attribution, auction mechanics, or optimization algorithms is a strong plus.

Leadership & Soft Skills

• Excellent stakeholder management and communication skills.
• Ability to translate technical concepts into business language.
• Strong decision-making and prioritization abilities.
• Passion for mentoring, coaching, and building high-performing teams.

What We Offer:

• Opportunity to build and lead the Data Science function of a global product company.
• Direct influence on product roadmap and business outcomes.
• Collaborative and international team environment.
• Competitive compensation package and growth opportunities.
• A chance to shape the future of mobile app monetization.

Location: We are flexible! Come and join us either in Hamburg, Berlin or Barcelona. 

Freenow by Lyft empowers smarter mobility decisions helping people to move freely and cities to thrive.

The Freenow marketplace is a real-time, two-sided platform connecting millions of riders and drivers. Its efficiency and scalability are powered by a sophisticated ecosystem of Machine Learning systems spanning key domains such as Pricing, Allocation, and Marketplace Payment&Risk.

In this role, you’ll help advance our MLOps practices, build the services and the infrastructure that scales ML models from research to real-time production and observe how they impact the daily lives of our users and our business.

Be ready to work in a multinational, diverse, highly motivated and collaborative team of passionate developers who strive for excellence and like to have fun doing it.  Are you ready for your next ride?

YOUR DAILY ADVENTURES WILL INCLUDE:

• Developing and maintaining high-performance, distributed systems that serve ML models at scale with low latency
• Collaborating with Data scientists and Software Engineers to solve business-critical problems in real-time.
• Streamlining online serving for classical and deep learning models to accelerate A/B testing cycles and reduce time-to-market
• Enhancing our MLOps platform capabilities to support AI state of the art
• Implementing ML Monitoring and Observability best practices, including automated data drift detection and self-healing system triggers
• Contributing to the evolution of our MLOps stack and practices.

Our Techstack: Python | Airflow | Java | Kubernetes | Kafka | Databricks |AWS & more

TO BE SUCCESSFUL IN THIS ROLE:

• M.S or B.S in Computer Science, Machine Learning, Mathematics or equivalent technical background with related field
• 3+ years of professional experience deploying, scaling and monitoring ML models in production environment 
• Proficiency in Python. Experience in JVM based programming language (Java, Scala, Kotlin) or GO is highly desirable
• Proven experience with large-scale data processing systems and modern data warehousing 
• Strong grasp of classical ML and Fundamental understanding of modern ML algorithms (e.g. DNN, RL, transformers ) and mathematical optimizations e.g LP, Convex Optimization
• Team player, and an avid learner, with a proven capacity to collaborate seamlessly with diverse teams, including those in data, tech, and product, as well as other stakeholders.
• Clear Communication in English. We are a very diverse team with people from all over the world and English is our language of communication, besides Python.
• Passionate about harnessing Artificial Intelligence and have the will to play an essential role in the future of urban transportation.
• Experience with streaming technologies and real-time data processing (e.g., Apache Kafka, Apache Flink, Feature Stores) is a plus.

BENEFITS & PERKS IN A NUTSHELL:

DIVERSITY, EQUITY & INCLUSION:

Freenow is an equal opportunity employer and we consider qualified applicants regardless of race, religion, national origin, gender, gender identity, sexual orientation, disability or age.
We want you to grow and evolve, bring your true self to work. 

ABOUT US:

Freenow by Lyft empowers smarter mobility decisions, helping people to move freely and cities to thrive. Through our multi-mobility app, we feature broad options for everyone across 9 European markets and over 180 cities. Millions of passengers can access services including taxis, private hire vehicles, carsharing, car rental, e-scooters, e-bikes, e-mopeds, and public transport within a single app. 

In July 2025 Freenow was acquired by Lyft, a global mobility platform, which connects riders and drivers for billions of rides across North America and Europe. Together, Freenow and Lyft are creating a more caring and connected world, with transportation for everyone. We are a global, diverse, highly motivated, and collaborative team that strives for excellence and likes to have fun. Ready for your next ride?

Senior Data Scientist — Verify V2 Data Products, Insights & Monetization

Mission

Build the quantitative foundation that proves and amplifies Verify v2's value—transforming verification telemetry into a reliable, customer-facing data infrastructure that demonstrates measurable ROI, optimizes channel economics, and lays the groundwork for an autonomous identity and verification platform.

You'll own the end-to-end data pipeline from raw events to customer-visible metrics that answer the question every customer asks: "What is this product actually worth to my business?"

What You'll Own

1. Customer Value Infrastructure (Prove ROI at Every Level)

Build the metrics that quantify customer-specific business impact:

• Design and maintain a real-time Customer ROI Engine calculating cost-per-successful-verification, fraud savings, conversion lift, and time-to-value by customer, segment, and use case
• Create customer-facing Value Dashboards showing verification success rates vs. industry benchmarks, cost efficiency trends, and projected savings
• Develop attribution models connecting verification outcomes to downstream business metrics (account activations, transaction completion, fraud prevented)

Establish pricing intelligence at the customer level:

• Build granular unit economics visibility: cost-to-serve, margin contribution, and channel mix efficiency per customer
• Model willingness-to-pay signals and usage patterns to inform tiered pricing and custom packaging
• Quantify the revenue impact of workflow configurations (Silent Auth-first vs. SMS fallback economics)

2. Channel Performance & Optimization (Make Every Verification Smarter)

Create a single source of truth for channel economics:

• Unified performance metrics across SMS, Voice, Email, WhatsApp, and Silent Authentication: deliverability, latency, conversion rate, cost-per-success, and failure taxonomy
• Country × carrier × channel performance matrices with confidence intervals and anomaly flags
• Real-time channel health monitoring with automated alerting for degradation

Build the intelligence layer for workflow optimization:

• Predictive models for optimal channel routing (next-best-channel given geography, time, customer segment, historical performance)
• Fallback effectiveness analysis: quantify conversion recovery and cost trade-offs for each fallback path
• Silent Authentication signal analysis: success/rejection drivers, speed benchmarks, and UX impact measurement

3. Product Data Platform (Foundation for Autonomy)

Design data architecture that enables autonomous decision-making:

• Define the canonical event schema and taxonomy for all verification touchpoints (API calls, webhook events, workflow steps, outcomes)
• Build certified, versioned datasets powering self-serve analytics, ML models, and customer-facing products
• Implement data quality infrastructure: lineage tracking, anomaly detection, freshness SLAs, and automated reconciliation

Ship ML/analytics products that move toward autonomous verification:

• Conversion propensity models: predict verification success probability in real-time to optimize routing
• Fraud & abuse detection: anomaly scoring for traffic pumping, IRSF patterns, and bot behavior—with automated response recommendations
• Time-to-verify prediction: forecast completion time to enable SLA commitments and dynamic timeout tuning
• Customer segmentation: behavioral and commercial clustering for personalized workflows and pricing

4. Monetization (Turn Data into Revenue)

Develop data products that customers will pay for:

• Verification Intelligence Suite: premium analytics, industry benchmarks, and deliverability diagnostics
• Workflow Optimizer: ML-driven recommendations for channel sequencing, timeout configuration, and fallback strategies by geography and vertical
• Fraud Protection Package: risk scoring, pumping detection, and abuse pattern alerts with quantified savings

Define commercial success:

• Package entitlements, usage thresholds, and upgrade triggers
• Track attach rates, retention lift, and expansion revenue attributable to data products
• Build the business case for each offering with clear ROI narratives

Key Responsibilities

• Own the customer value narrative: Build and maintain the infrastructure that lets every customer (and our sales team) articulate Verify's ROI in dollars and percentages
• Ship production ML systems: From feature engineering through deployment, monitoring, and iteration
• Create reliable, self-serve data products: Dashboards, APIs, and datasets that scale without manual intervention
• Drive pricing and packaging decisions: Provide the quantitative foundation for how we charge and what we bundle
• Partner across the organization: Work with Product, Engineering, Finance, Sales, and Customer Success to embed data into every decision
• Report to leadership: Own KPI narratives on margin drivers, growth levers, and competitive positioning

Success Measures

What "Great" Looks Like

Core Data Science

• Experimentation design and causal inference (A/B testing, CUPED, uplift modeling, instrumental variables)
• Predictive modeling: classification, survival analysis, time series, real-time scoring
• Anomaly detection with adversarial thinking (fraud patterns, traffic manipulation, abuse signals)
• Customer analytics: segmentation, LTV modeling, churn prediction, cohort economics

Data Engineering Fluency

• Strong SQL; Python (pandas, scikit-learn, PySpark); comfortable shipping production code
• Event-driven architecture: streaming pipelines and real-time analysis and adaptation (Apache Flink), webhook processing, idempotency, late-arrival handling
• Data modeling: star schemas, semantic layers, data contracts, metric certification
• MLOps: feature stores, model monitoring, CI/CD for analytics, orchestration (Airflow/Dagster)

Product & Commercial Analytics

• Pricing analytics: unit economics, willingness-to-pay estimation, margin optimization
• Funnel analysis for multi-step, multi-channel workflows
• Dashboard design and narrative clarity (Looker, Tableau, dbt metrics layer)
• Packaging and monetization strategy for data products

Domain Expertise (Highly Valued)

• CPaaS, verification, or 2FA: OTP mechanics, deliverability constraints, carrier relationships
• Silent Authentication: network-based verification, success/rejection drivers, integration patterns
• Fraud and risk: traffic pumping, IRSF, bot detection, abuse economics
• Privacy and compliance: GDPR/CCPA, data minimization, audit requirements, customer-facing data controls

Background

• 5–8+ years in data science/analytics, with ≥2 years building and shipping data products
• Track record of translating ambiguous business questions into measurable outcomes
• Experience in B2B SaaS, identity/auth, fintech, messaging/telecom, or fraud analytics preferred
• Demonstrated ability to influence product and pricing decisions with data

Why This Role Matters

Verification is shifting from a cost center to a strategic differentiator. The data infrastructure you build will:

1. Prove value — Give every customer undeniable evidence of ROI
2. Optimize economics — Make every verification faster, cheaper, and more reliable
3. Enable autonomy — Lay the foundation for a platform that routes, optimizes, and protects without human intervention

You'll shape how Vonage—and our customers—think about identity verification as a measurable, optimizable, intelligent system.

#LI-WW1

Why Engineering at Dataiku?

Dataiku’s on-premise, cloud, or SaaS-deployed platform connects many data science technologies, and our technology stack reflects our commitment to quality and innovation. We integrate the best of data and AI tech, selecting tools that truly enhance our product. From the latest LLMs to our dedication to open source communities, you'll work with a dynamic range of technologies and contribute to the collective knowledge of global tech innovators. You can find out even more about working in Engineering at Dataiku by taking a look here.

Here are some useful links so you preview what we do at Dataiku: Dataiku's Key Capabilities ; Dataiku's Github, you can also take a look at the Gallery, a public instance showcasing some example projects (note editing is very limited and will be regularly reset). 

Our product is called Dataiku DSS which stands for Dataiku Data Science Studio. If you’d like to know more about it, you can watch the demo here or try the free version here.

How you’ll make an impact

This position is either onsite/hybrid from our Berlin office or full remote from any part of Germany.

As a Fullstack Engineer, you’ll contribute to building Dataiku DSS core features by joining one of the following themes:

• Data Preparation: Develop tools for data integration, transformation, and cataloging, as well as Jupyter notebooks, SQL workbenches, and APIs.
  • Add new capabilities for data integration, transformation, and visualization using LLMs
  • Optimize workflows for large-scale datasets and improve database support
  • Strengthen developer tools like Jupyter notebooks and SQL workbenches
  • Tech stack: Java, Python, AngularJS, Angular

• AI & Machine Learning: Collaborate on next-gen AI features, from statistics and time series forecasting to LLM inference.
  • Build next-generation features like cross-provider LLM APIs or integration with the latest Machine Learning models
  • Collaborate with research teams on innovative machine learning experiments
  • Tech stack: Python, scikit-learn, TensorFlow, PyTorch, Java, Angular, AngularJS

• Data Consumption: build the experience that makes data accessible and actionable for everyone.
  • Enhance our collaborative Workspaces to organize and explore charts, dashboards, datasets and more
  • Create innovative interfaces that answer business questions and surface relevant insights using LLMs
  • Tech stack: Angular, AngularJS, Java, Python

• Data Visualization: elevate our data visualization capabilities with high-performance charting tools and dashboards.
  • Develop new chart types and optimize visualization performance
  • Enhance dash boarding capabilities to deliver fast and flexible experiences
  • Tech stack: React, AngularJS, Angular, D3.js, ECharts, Node.js, Java, Spring
    
    
• MLOps: Develop back-end capabilities for automating and monitoring ML model lifecycles while supporting cross-functional collaboration.
  • Build tools to automate retraining, monitoring, and deployment of ML models
  • Enhance collaboration features for ML stakeholders
  • Tech stack: Python, Java, Angular, Angular.JS, Kubernetes, Docker
    
    
• Platform: Focus on scaling and securing the platform while improving cloud integrations and supporting a wide array of data sources and engines.
  • Optimize processing engines for scalability and latency
  • Expand capabilities to support new databases and cloud platforms
  • Tech stack: Java, Python, Angular, Kubernetes, Spark, AWS, Azure, GCP
    
    
• AI Governance: Build features for AI compliance, connecting disparate systems and simplifying governance processes.
  • Develop customizable platforms for managing AI compliance and governance
  • Simplify policy enforcement and integration across disparate systems
  • Tech stack: Java, Angular, PostgreSQL

What you need to be successful 

• We’re stack agnostic, so all you need is a significant experience in software engineering,  building a real product
• We’re hiring at all levels (junior, mid, senior)
• A passion for combining backend performance with exceptional frontend user experience.
• You want to work in a fast-paced, high-growth environment that values diversity of talent, excellence of product, and exciting engineering challenges at scale

What does the hiring process look like?

• Initial call with a member of our Technical Recruiting team (45min)
• Video call with an Engineering Team Lead (1h30)
• Technical Assessment to show your skills (Home Test or Live Coding)
• Final Interviews with our VPs of Engineering (2h)   #LI-Remote

About The Role 💻

We are looking for a Senior MLE Staff Engineer who is an expert in the deployment and scaling of LLMs and Agentic systems. In this role, you will bridge the gap between machine learning experimentation and production at scale by building the robust, highly efficient platform that powers our AI initiatives.

While our Data Science teams focus on developing and tuning state-of-the-art models, you will be the owner of the platform that enables them. You will define best practices, build automated pipelines, and ensure that our infrastructure can handle complex agentic workflows with high reliability and performance. This is a role for a visionary who stays ahead of the daily shifts in the AI landscape and can rapidly adapt our stack to leverage emerging trends.

For more insight into the technologies used by the engineering team at Clarity AI, please explore our Tech Stack

What You’ll Be Doing 🚀

As a Senior MLE Staff Engineer, you will be responsible for:

• GenAI Platform Engineering: Designing and developing the core platform that enables the efficient deployment, scaling, and management of LLMs and multi-agent systems.
• Infrastructure for Agents: Building specialized infrastructure to support long-running agentic workflows, including state management, tool-calling interfaces, and complex reasoning loops.
• High-Scale Productionization & Model Serving: Scaling inference for LLMs to handle global demand while optimizing for latency, throughput, and cost. Implement standard batch and online serving with controlled rollback.
• Build & Delivery: Establishing the "Golden Path" for model deployment through a self-service path to move code, data, and models to production safely and reproducibly, including automated evaluation frameworks, safety guardrails, and CI/CD/CT pipelines.
• Strategic Vision & Product Management: Continuously monitoring the AI ecosystem and proactively evolving our platform to maintain a competitive edge. This includes adopting best practices in Platform Product Management and driving the adoption of golden-path solutions.
• End-to-End Observability: Implementing deep observability for LLMs, tracking not just system health but providing unified visibility into health, impact, and root cause across data, ML, and GenAI (including model hallucinations, token usage, and RAG performance).
• Collaborative Foundation: Providing the tools and abstractions that allow Data Scientists and stakeholders to move from a "tuned model" to a "production service" with zero friction.

Location 🌍

The role is based in our tech hub in Madrid, Spain, but we are remote-friendly and open to the CET timezone +/- 2 hours.
Way of Working: Remote/Hybrid

What You’ll Need 👀

• LLM & Agent Expertise: Deep, hands-on experience deploying Large Language Models and complex agentic architectures at scale.
• GenAI Platform Specifics: Proven experience in implementing Prompt Lifecycle Management (versioning, testing, and deploying prompts as code), an LLM Abstraction Layer (provider-agnostic access), and systems for Cost & Usage Control (visibility and limits on GenAI spend per use case).
• Evaluation & Benchmarking Mastery: Expert-level experience building automated evaluation pipelines and frameworks (e.g., Ragas, DeepEval, G-Eval) and implementing LLM-as-a-judge patterns to validate model quality, grounding, and safety in CI/CD.
• Platform & MLOps Mindset: A proven track record of building platforms or shared infrastructure. Deep understanding of MLOps concepts like Model Registry (versioning, state management, and lineage) and Model Monitoring & Drift Detection.
• 3+ years of experience in MLOps or high-scale Software Engineering with a focus on AI production environments.
• Technical Stack Mastery: Expert-level Python and deep experience with container orchestration (Kubernetes, Docker) and cloud infrastructure (AWS/GCP).
• AI Tooling & Frameworks: Proficiency with orchestration libraries (e.g., LangChain, LlamaIndex, CrewAI), vector databases (e.g., Pinecone, Weaviate), and inference engines (e.g., vLLM, TGI).
• Agility & Adaptability: The ability to learn and implement new technologies in a field that changes weekly. You should be a "fast mover" who enjoys constant evolution.
• Software Excellence & Governance: Strong fundamentals in API design, microservices, and "GitOps" methodologies, including the implementation of automated security and compliance by default.
• English Proficiency: Excellent communication skills (minimum C1 level), with the ability to articulate technical vision to both engineers and leadership.

What We Offer 🥁

• Competitive compensation, both in terms of base salary as well as equity plans that enable to you to share in our success
• Flexibility in ways of working both in terms of your schedule as well as your location, whether you prefer to work from home, the office, or abroad with access to a global network of co-working spaces 
• Generous paid time off schemes, including vacation, sabbatical, religious observance and compensation days
• Meaningful benefits including private healthcare coverage, fitness and wellness programs covered through Wellhub, working-from-home allowances to help you set up your home office and cover monthly expenses
• Professional development with annual training budget for conferences, courses, certifications and access to top market e-learning platforms
• Collaborative environment with multiple offices around the globe, regular team activities and events as well as employee-led resource groups

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive.

By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.

As part of our team, you will:

• Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
• Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
• Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
• Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
• Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.

We’ll be happy to have you on our team if you have:

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• A solid understanding of the ML lifecycle from idea to the customer-facing application.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
• Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
• At least three years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow.
• Developing infrastructure components and services using cluster solutions like Kubernetes.
• The development of Python-based backend services.
• Creating and maintaining ML pipelines, including legacy ones.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
• Writing and maintaining Python libraries used by internal (or external) ML engineers.
• A strong theoretical background in NLP and transformer-based approaches.
• Writing code in Java and/or Kotlin.

#LI-HYBRID
#LI-MR1

At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the world’s most robust and effective developer tools. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

We are working on an ambitious new platform that provides AI capabilities to all JetBrains products. Our platform is based on models developed in-house for writing and coding assistance, as well as integration with our strategic partners.

We are looking for a Research Engineer who can contribute to training foundation models for coding tasks. You’ll be working on developing Large Language Models from scratch and deploying them into production environments where they will be accessible by end users across the globe.

We value engineers who: 

• Can plan projects and make decisions independently, consulting with others if needed.
• Identify customer needs and prioritize their tasks accordingly.
• Start with the simplest solutions and gradually add complexity as needed.
• Take sole responsibility for an entire subsystem.
• Have a passion for learning and a desire to stay up to date with the latest developments in the LLM field.

In this role, you will: 

• Work with stakeholders to convert business requirements into technical specifications.
• Train LLMs from scratch on a large GPU cluster.
• Collect and process pre-training and fine-tuning datasets.
• Support and improve existing subsystems.

We’ll be happy to have you on our team if you have: 

• Experience in design, deployment, and support of production ML systems.
• A strong theoretical background in NLP and transformer-based approaches.
• Proficiency with modern deep learning frameworks such as PyTorch and common libraries for NLP.
• Experience in distributed training of multi-billion parameter models.
• Attention to detail in everything you do and great communication skills.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, TensorRT.
• LLM alignment techniques such as RLHF/RLAIF.
• MLOps tools and practices, including CI/CD for ML.
• K8s and Kubeflow.
• Scientific publications in the NLP field.

How we develop JetBrains AI: 

• A cluster of hundreds of NVIDIA GPUs as training infrastructure.
• Git for source control management.
• Python, PyTorch, and HuggingFace as an ML stack.
• Kubeflow and Weights & Biases for experiment tracking.
• TeamCity as a CI Automation system.

#LI-KP1

At JetBrains, code is our passion. Ever since we started back in 2000, we have been striving to make the world’s most robust and effective developer tools. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

JetBrains is evolving beyond standalone developer tools toward a unified, AI-native platform for software development.

AI is no longer just an assistant inside the editor – it is becoming an active participant in how software is planned, built, reviewed, and operated across teams and organizations. This shift introduces new challenges that cannot be solved at the level of individual tools alone: governance, security, cost control, observability, and coordinated work between humans and autonomous agents.

Our goal is to build a platform that enables companies to adopt AI in software development in a structured, scalable, and economically efficient manner without locking them into closed ecosystems. This platform will serve as the execution and governance layer for AI-driven development, deeply integrated with developer tools but designed to work across teams, products, and environments.

We are looking for an experienced ML leader who has created products with an ML backbone, weaving together research, technical excellence, and strong product focus.

We are seeking a professional who excels in three key areas: technology, product vision, and business operations. This role involves extensive cooperation with products across the company – both AI-native and just integrating AI.

In this role, you will: 

• Identify, prioritize, and champion high-impact AI projects that align with JetBrains' product strategy and deliver measurable business value.
• Lead a team of about 50 people across multiple AI initiatives, including hiring and developing team leads, ML engineers, and MLOps specialists.
• Set the technical direction for JetBrains’ ML roadmap,  translating cutting-edge AI advancements into practical product innovations.
• Ensure projects deliver on commitments and create measurable value.
• Set technical standards, architectural direction, and best practices for ML development.

We’ll be happy to have you on our team if you have: 

• The ability to balance long-term R&D with short-term product needs.
• A track record of shipping production ML systems that users actually use.
• Experience building business cases for technical projects.
• A deep understanding of modern ML infrastructure, LLMs, and AI tooling.
• Outstanding stakeholder management skills and are comfortable working with senior leaders.
• The ability to thrive in a fast-paced environment, effectively managing multiple projects simultaneously.
• Excellent management and communication skills.
• Attention to detail in everything you do.

We’d be especially thrilled if you have experience with: 

• Distributed training of multi-billion parameter models.
• CI systems, workflow automation, and experiment tracking.
• The Kotlin programming language.

#LI-KP1 #LI-HYBRID

We are currently looking for a Senior Data Engineer to join us remotely in Spain. You will work with our EMEA and US customers to help them solve their data engineering, ML, ML Ops, and cloud migration puzzles.

"As a Data Engineer at DATAPAO, you will have the unique opportunity to push the boundaries of technology and lead the way in how data transforms the world. We work with customers who want to innovate and change their industries. So it never gets boring! Sorting out a complex technical puzzle, setting up a new data architecture from scratch, or guiding our customers through adopting the latest technologies is our routine here. Hence, we'd expect you to be more than just a (data) engineer. We'd expect you to breathe and live data, to thrive for knowledge and excellence, and to be keen to build something enduring and impactful for our customers. We'd expect you to be a (data) Paoneer!"  (Máté Gulyás, CEO)

What will you do?

As a Senior Data Engineer, you are expected to deliver (across industries) on some of our most complex projects - individually or by leading small delivery teams. Although we work for the biggest multinational companies where years-long behemoth projects are the norm, our projects are fast-paced, typically 2 to 4 months long. Most are delivered using Apache Spark/Databricks on AWS/Azure and require you to directly manage the customer relationship alone or in collaboration with a Project Manager. Additionally, at this seniority level, we expect you to compound your impact across the team and the organization - by supporting our pre-sales process, mentoring more junior engineers, and contributing to hiring best-in-class data talent.

What does it take to fit the bill?

Technical Expertise

 Consulting & Client-Facing Skills

Operational Readiness & Soft Skills

• In a fast-paced consulting environment, where no two days are the same, flexibility and problem-solving comes naturally to you. Furthermore, resilience and thriving on challenges are essential traits in our industry;

What do we offer?

You'll have the rare opportunity to join DATAPAO at a time when you can still meaningfully impact our growth trajectory and organizational success. Furthermore, as our first-ever Spain hire, you could meaningfully contribute to expanding our local presence and, eventually, establishing a physical hub in this geography.

Surely, we know everyone needs a little something to do their best work. Here is a list of some other extras we offer:  

• The chance to work remotely - as we don't have a physical office in Spain yet, we offer a fully remote setup for the time being. However, given our strong community emphasis, we'd expect you to spend one month in our Budapest hub during onboarding and visit the office for one week every quarter;

• World-class educational benefits - there’s no shortage of learning opportunities at DATAPAO, meaning that you'll get access to Databricks' public and internal courses to learn all the tricks of Distributed Data Processing, MLOps, Apache Spark, Databricks, and Cloud Migration from the best. Additionally, we'll pay for various data & cloud certifications, you'll get dedicated time for learning during work hours, and access to our internal library; 

• Flexible PTO days - we’re flexible with how many days you take off if that does not conflict with customer success and the work getting done;

• Fully paid Medical Leave for up to 15 days per year - because when you’re unwell, we want you to focus on recovery and not worry about your paycheck;

• Comprehensive private health insurance plan via Allianz - offered via our EOR (Employer of Record) provider, Remote.com. Remote will be your official employer in Spain for the foreseeable future until we establish our own legal entity in Spain;

• Employee Assistance Program (EAP) via Allianz & Remote - life always happens! And we'd like to be there for you when it counts the most. Through EAP, we offer our employees access to psychology, financial, and legal consulting services via phone or in person;

• Top-notch working equipment - working comfortably means working well. You will get a MacBook Pro and a sign-up bonus of 500 EUR gross (to be paid with your first payroll) to set up your home office or whatever else is needed to give your best;

• The chance to make an impact in a high-transparency culture - open feedback and no politics are things we value a lot at DATAPAO. Additionally, you'll get the chance to work in the field of data & AI and shape the future of a rapidly growing tech company.

IMPORTANT NOTE: Given that we are still sorting our way in Spain, this list will likely get an update in the following months, and we're also committed to growing it with us over time.

The gross salary for this position ranges from 70.000 to 80.000 EUR/year, depending on your assessed seniority level during the selection process.

Please note that currently we are unable to support transfer or offer full sponsorship for Skilled Worker visas as we’re just setting foot into Spain.

We are seeking a skilled Data Engineer to join our Mobile Growth Platform UA team in Barcelona. The ideal candidate will be responsible for building, maintaining, and optimizing our data infrastructure, ensuring seamless data flow and accessibility for ML and DA, analytical and business needs.

Key Responsibilities:

• Design and build scalable data pipelines: Architect, construct, and maintain robust ETL/ELT pipelines using the Databricks platform (Apache Spark, Delta Lake, Delta Live Tables) for batch and streaming data ingestion from various sources.
• Manage data architecture: Define and maintain a scalable and secure data platform architecture that integrates Databricks (for data lakehouse management and complex transformations) with specialized OLAP engines (ClickHouse/Druid for fast querying).
• Design, develop, test, and orchestrate data workflows to streamline pipelines.
• Optimize performance and reliability: Monitor, troubleshoot, and fine-tune data workflows and database performance (e.g., Spark job optimization, ClickHouse schema tuning, Druid data partitioning) to ensure maximum efficiency and reliability.
• Contribute to the development of internal tools for automating data processes and business workflows, enhancing efficiency and scalability.
• Collaborate closely with product, MLOps and data science teams to ensure an optimal data experience for all ML practitioners and data consumers.

Requirements:

• 3-5+ years of professional work experience in data engineering.
• Strong expertise in Python and common data frameworks (Pandas, PySpark).
• Experience with modern data storage solutions: Data lakes (Delta tables), and data warehouses.
• Experience with data orchestration tools (Dagster, Airflow).
• Experience with streaming technologies (Kafka, Spark structured streaming).
• Experience with cloud platforms (AWS S3, EC2 etc.)
• OLAP Databases: Hands-on experience with ClickHouse and/or Apache Druid for real-time analytics and low-latency data serving.
• Strong problem-solving skills and ability to excel in a fast-paced startup environment.
• Based in Barcelona or ready to relocate to Barcelona.

Nice to have:

• Experience with Databricks platform.
• Experience in building ML data platforms.