Groupon is mid-transformation. Seven parallel streams. Real targets: contact deflection, resolution rates, repurchase. The streams are moving - but each one needs a single accountable person who can both diagnose the problem and ship the fix. That's this role. You won't be coordinating. You won't be waiting for an analyst or speccing things out for an engineer. You'll pull the data yourself, write the prompt yourself, build the dashboard yourself, and hold people accountable without having authority over them. If it's blocking you, you do it.

Who you'll work with

You'll report to Maguelonne, Director of Operational Transformation. Your closest partners will be teams across Customer Service, Merchant Operations, Product, Engineering, Supply, and Content  with key stakeholders based in Madrid and Prague.

What you'll be doing

• Owning transformation streams end-to-end - defining the problem from raw data, building the delivery plan, assigning owners, setting milestones, and tracking progress in dashboards you build yourself
• Building AI workflows from scratch - writing prompts, running evals, validating output against real cases, improving and scaling
• Pulling and interrogating your own data - building your own reports and business cases without waiting for support
• Holding cross-functional teams accountable - naming blockers early, escalating fast, and not letting "we don't own that" become an excuse for slow delivery
• Finding creative fixes - many people here will say "we tried that before." Your job is to zoom out, find the quick win, and simplify what others have overcomplicated
• Surfacing risks before they become problems - keeping stakeholders aligned without over-meeting

What success looks like

At 30 days: You've personally pulled and analysed the data on your stream. You've shipped at least one small automation or prompt-driven workflow. You know the landscape — the streams, the blockers, the key players.

At 60 days: You own at least one stream with a clear delivery plan. You've prototyped one AI workflow running on real data. Stakeholders come to you when things are stuck.

At 6 months: Contact deflection rate is improving on your stream. At least one AI workflow is in production and being used daily. Your manager doesn't have to chase status — you surface risks before they become issues.

What this role demands

You can demonstrably:

• Ship a production-grade LLM workflow end-to-end - prompt design, tool use, evaluation, edge case handling. A Zapier flow doesn't count.
• Identify an operational problem, form a view on how to fix it, and execute without being pushed
• Hold cross-functional stakeholders to commitments without authority over them — and find structural levers when persuasion alone doesn't work
• Navigate ambiguity and create structure where none exists, while knowing when a decision needs escalation and when it just needs to be made
• Re-plan without drama when priorities shift and not lose the thread of what matters

What we are looking for

• 2-4 years in operations, program management, or strategy & ops roles where you personally shipped - not just managed others who shipped
• At least one production LLM workflow built and running end-to-end
• Comfortable pulling, interrogating, and presenting data without support
• Assertive communicator who names blockers and drives resolution
• Fluent English, based in Madrid or Prague

Nice to have

• Experience at a fast-paced tech or marketplace company
• Familiarity with Customer Service or Merchant Operations contexts
• Active follower of the AI/LLM space - you've tried the tools yourself and have an opinion on which to use when

The hiring process

• TA screen with your recruiter (30 min)
• Hiring manager interview (45 min)
• Case study (3-5 days to complete)
• Case study presentation (45 min) - you walk us through your approach, findings, and recommendations, followed by Q&A

The transformation agenda is real, the exec visibility is high, and the scope is broader than most roles you'll find at larger companies. If you want to move fast, ship AI into production, and be the person who actually unblocks things - apply.

THE ROLE

Join the Infrastructure and Shared Services (ISS) team as a Security Engineer to build and defend the next generation of the Everpure Platform. Whether your expertise lies in network architecture, detection engineering, or securing AI pipelines, you will play a critical role in protecting our engineering infrastructure from lateral movement and advanced threats. You’ll collaborate directly with Platform Reliability and Infrastructure teams to bake security into the automation that powers our global developer services. This is a high-impact, hands-on engineering role focused on creating a resilient, zero-trust environment for our most critical services.

WHAT YOU'LL DO

• Engineer Advanced Defense Patterns: Design and implement zero-trust network access models, secure service-to-service communication, or AI-specific guardrails to eliminate unauthorized data access and lateral movement.
• Operationalize Detection & Response: Build high-fidelity behavioral detections for infrastructure and identity systems, ensuring all security telemetry is integrated into our SIEM for real-time visibility and rapid incident containment.
• Validate & Harden Infrastructure: Conduct threat modeling and security validation exercises (including ransomware simulations) to verify the effectiveness of controls across Kubernetes, cloud-native workloads, and AI model pipelines.
• Automate Security at Scale: Partner with engineering teams to integrate security controls directly into infrastructure-as-code and CI/CD pipelines, automating log analysis and network policy enforcement to reduce manual overhead.

WHAT YOU BRING

• Advanced knowledge of securing distributed systems, with deep technical proficiency in either network protocols (TCP/IP, TLS/SSL), detection engineering methodologies, or AI/ML pipeline security (protecting against prompt injection and data leakage).
• Hands-on experience securing cloud-native environments (AWS/Azure/GCP), containerized workloads (Kubernetes), and modern infrastructure-as-code automation.
• Ability to distinguish legitimate operational activities from indicators of compromise through deep log analysis, packet inspection (Wireshark), or behavioral modeling in SIEM/XDR platforms.
• Proven track record of working alongside DevOps, SOC, and Data teams to drive security outcomes without compromising engineering velocity.
• We are primarily an in-office environment and therefore, you will be expected to work from the Prague office in compliance with Everpure’s policies, unless you are on PTO, or work travel, or other approved leave.

  #LI-ONSITE

THE ROLE

Our Senior Applied AI Engineer builds and operate production-grade AI systems that extract meaning from large-scale unstructured document collections, enabling enterprise data discovery classification, and governance. 

This role owns the full lifecycle of graph intelligence solutions — from problem definition and data modelling, to building and enriching knowledge graphs, and deploying ML- and LLM-assisted analytics in production. The focus is on semantic and contextual analysis of unstructured data to uncover relationships, patterns, and insights that support AI safety, security, and compliance requirements.

WHAT YOU'LL DO

• Design, build, and deploy graph-based AI solutions, combining knowledge graphs, LLMs, and ML models applied to large-scale unstructured data
• Define and own data pipelines that extract, transform, and enrich entity relationships into production-grade knowledge graphs
• Integrate LLMs and ML models into text processing pipelines for classification, embedding generation, document similarity, and semantic analysis 
• Design, deploy, and operate graph and vector databases to support retrieval, reasoning, and analytics
• Optimize models and inference pipelines for production constraints including latency, throughput, cost, and infrastructure
• Deploy, monitor, and iterate on ML systems in production environments ensuring reliability and continuous integration
• Drive architectural decisions and technical direction for applied AI and graph intelligence solutions
• We are primarily an in-office environment and therefore, you will be expected to work from the Prague office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• Proven experience in developing and deploying to production knowledge graphs in a real business scenario
• Strong hands-on background in developing text-based ML and LLM systems, including prompt context engineering, optimized for production environments
• Strong proficiency in Python, ML / KG frameworks and tools (e.g. Neo4j, NetworkX, Node2Vec, SentenceTransformers) with the ability to select the right tool for the problem at hand
• Ability to apply the appropriate graph algorithms and design choices for problem solving, including relationship and ontology modelling, feature definition, supervised vs unsupervised graph analytics
• Ability to design clean, modular, and testable ML code in a collaborative environment
• Experience with MLOps practices, building production-grade Docker images, and development of AI solutions designed for integration into production systems
• Experience collaborating with software engineers, product managers, and business stakeholders
• Strong communication skills and ability to explain ML concepts and system behavior clearly

 #LI-ONSITE

ZAM Network is a multi-faceted company based in the United States. We specialize in building engaged gaming communities through its three core pillars of marketing, creative services and user research. ZAM operates one of the largest gaming networks in the world including the #1 community for World of Warcraft, Wowhead. 

Wowhead was created with one sole intent: providing World of Warcraft players with tools and information to make their gameplay more enjoyable. All staff members are passionate fans of World of Warcraft, which helps us keep abreast of the most important features other players are interested in! We pride ourselves on our comprehensive database with helpful user comments, popular tools like the Dressing Room and Flying Tracker, lightning-fast datamining to uncover the latest test realm updates, and detailed guides written by professional players on all popular WoW topics including classes, collections, secrets, and raids. We’ve branched out to cover the Diablo franchise and are planning our next stage of growth.

We are currently seeking a Reverse Engineer with extensive data mining experience to join our team as an independent contractor. This is not an entry level role and you will be working to extract both static and runtime game data for World of Warcraft to help Wowhead to expand our current tools to aid our community of gamers. This position is a full-time remote as you will collaborate with the entire Wowhead team as we work together to deliver world-class products to our fans.

Key Responsibilities

• Manage development of the Wowhead Studio application, to facilitate datamined assets of creatures, characters, and other in-game objects
• Extract game data and game assets required to build tools to support our products and services.
• Maintain Wowhead’s model tools using the site’s WebGL model viewers. TypeScript-based standard viewer used in most of Wowhead website.
• Use C++ web assembly-based viewer that is faster with accurate lighting, for various World of Warcraft Player Housing pages.
• Use JavaScript-based Three.js model viewer.
• Maintain comprehensive technical documentation on game data (contents, formats, change history), data mining tools and processes employed, and anything else related to the role.
• Communicate consistently, clearly, and efficiently all assigned task progress, blockers, or any other role-related issues in a prompt and professional manner on a regular basis.

Experience and Technical Requirements

• Previous hands-on experience doing data mining work on World of Warcraft and/or Diablo IV. This is not a “learn as you go” type of role; we operate in a fast paced, highly competitive, and challenging environment.
• Comprehensive understanding using various programming languages (such as C++, Python, C#, TypeScript and JS) in order to choose the best tool for the job.
• Understanding of various debugging, reverse engineering, and custom made tools to accomplish assigned tasks.
• Ability to read and comprehend x86/x64 assembly language, such as via IDA as this role is not solely a “file format” data mining role.
• Strong communication skills in English, both written and verbal, with the ability to convey information clearly and effectively.
• A collaborative mindset; ability to collaborate remotely and coordinate with stakeholders across different time zones.
• Professionalism, reliability, and a commitment to delivering high quality work utilizing your analytical and problem-solving skills.
• Being experienced in Blizzard games will make it easier to understand project requirements and the consumer side.

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 now building services and agentic tools that provide AI coding agents and end users with deeper context about codebases. Our code retrieval service already delivers meaningful improvements in agent speed and task performance, and we aim to push this further – extracting richer insights than snippets alone at the scale of several hundred thousand repositories. 

We are looking for an AI Engineer who can design and implement agentic tools from scratch, bring them to the end users, and make coding with agents smarter, faster, and more reliable.

In this role, you will:

• Translate product needs into agentic tools and services and ship them to production.
• Scale prototypes to large numbers of users.
• Improve and test our semantic search service.
• Build evaluation pipelines for code retrieval and coding task performance.

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

• At least five years of hands-on NLP experience.
• Experience in information retrieval systems.
• Strong experience in LLM prompt and tool engineering, as well as with AI agents.
• Solid programming skills in Python and/or Kotlin (and the willingness to learn the language you’re unfamiliar with).
• Experience building and operating production-grade software systems.

We’d be especially thrilled if you have:

• Experience developing in Kotlin or Java.
• Previously worked with code intelligence (e.g. AST, symbol graphs).

#LI-DNI

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

At JetBrains, code is our passion. Ever since we started, back in 2000, we have strived 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.

AI features in JetBrains IDEs, developed by the IntelliJ AI team, have quickly become a core part of how developers work inside our IDEs. The IntelliJ AI team partners with product groups across JetBrains to embed advanced AI features that accelerate developer workflows and deliver real value to software engineers.

We are currently looking to hire a Senior Machine Learning Engineer to help us realize our ambitious vision of creating AI assistance that supports the entire development lifecycle across JetBrains IDEs. If selected, you will join the ML subteam within IntelliJ AI, driving the development of our ML system from end to end by defining evaluation and metrics, shaping context orchestration, and helping product teams tailor AI capabilities to their needs.

In this role, you will:

• Design and drive evaluation frameworks for AI features, including metrics, experiments, and agent trace analysis.
• Diagnose model performance issues (e.g. prompt drift, context mismatches, and latency/quality trade-offs) and translate findings into actionable improvements.
• Experiment with contexts and lightweight models to continuously develop our ML system.
• Act as the ML liaison for product teams across JetBrains, adapting and scaling AI capabilities in JetBrains IDEs to their needs.
• Build and maintain small helper models (e.g. re-rankers, classifiers, embedding models) to support domain-specific tasks.
• Collaborate with colleagues in ML, product, engineering, and analytic teams to deliver improvements and monitor their impact in production.
• Stay up to date with research in the fields of LLMs, agents, and evaluation, bringing best practices into our workflows.
• Mentor junior engineers and help shape team culture, processes, and tooling around experimentation and evaluation.

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

• Have 5+ years of experience as an ML Engineer, with a solid background in production-grade ML systems (especially LLMs and agent architectures).
• Have experience with LLM evaluation methods and frameworks.
• Can design and run end-to-end experiments – hypotheses, metrics, data collection (including traces/logs), analysis, and decision-making.
• Are skilled in context-aware pipelines or conversational/agent systems.
• Have strong Python programming skills.
• Bring hands-on experience in fine-tuning or training smaller models (e.g. domain-specific fine-tuning and lightweight customizations).
• Communicate clearly and effectively across teams, translating ML/AI insights into product features.
• Have prior mentorship experience with ML/evaluation engineers.
• Thrive in a cross-functional, fast-moving environment, taking ownership, iterating quickly, and delivering results.

We’d be especially thrilled if you have: 

• Familiarity with agent-based systems and orchestrating multi-step reasoning agents.
• Experience with the Kotlin programming language.

#LI-MR1