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

Spektrum supports apex purchasers (NATO, UN, EU, and National Government and Defence) and their Tier 1 supplier ecosystem with a wide range of specialist services. We provide our clients with professional services, specialised aerospace and defence sales, delivery, and operational subject matter expertise. We are looking for personnel to join our team and support key client projects.

Who we are supporting 

The NATO Communication and Information Agency (NCIA) is responsible for providing secure and effective communications and information technology (IT) services to NATO's member countries and its partners. The agency was established in 2012 and is headquartered in Brussels, Belgium.

The NCIA provides a wide range of services, including:

• Cyber Security: The NCIA provides advanced cybersecurity solutions to protect NATO's communication networks and information systems against cyber threats.
• Command and Control Systems: The NCIA develops and maintains the systems used by NATO's military commanders to plan and execute operations.
• Satellite Communications: The NCIA provides satellite communications services to enable secure and reliable communications between NATO forces.
• Electronic Warfare: The NCIA provides electronic warfare services to support NATO's mission to detect, deny, and defeat threats to its communication networks.
• Information Management: The NCIA manages NATO's information technology infrastructure, including its databases, applications, and servers.

Overall, the NCIA plays a critical role in ensuring the security and effectiveness of NATO's communication and information technology capabilities.

The program

Assistance and Advisory Service (AAS)

The NATO Communications and Information Agency (NCI Agency) is NATO’s principal C3 capability deliverer and CIS service provider. It provides, maintains and defends the NATO enterprise-wide information technology infrastructure to enable Allies to consult together under Article IV, and, when required, stand together in the face of attack under Article V.

To provide these critical services, in the modern evolving dynamic environment the NCI Agency needs to build and maintain high performance-engaged workforce. The NCI Agency workforce strategically consists of three major categorise's: NATO International Civilians (NIC)'s, Military (Mil), and Interim Workforce Consultants (IWC)'s. The IWCs are a critical part of the overall NCI Agency workforce and make up approximately 15 percent of the total workforce.

Role ID – 2026-0061

Role Background

• To strengthen the Alliance through connecting its forces, NATO Communication and Information Agency (NCIA) delivers secure, coherent, cost effective and interoperable communications and information systems in support of consultation, command and control and enabling intelligence, surveillance and reconnaissance capabilities, for NATO, where and when required.
• The Ballistic Missile Defence Programme Office (BMD PO), as part of the NCIA, ensures the harmonised planning, implementation, deployment, evolution and support of the BMD capability, in a way that satisfies the operational requirements and minimises the military risk, taking into account the NATO political, economic and schedule requirements and using the most suitable industrial and technological solutions.
• ACT has completed a comprehensive series of IAMD studies and is now transitioning into the benefit-realisation phase. Building on these foundations, ACT intends to initiate a two-year Feasibility Study to expand the ambition of the Emerging Concepts and Technologies work package already defined in the BMD PRD 2026. The first year will focus on (T)BMD use cases and operational challenges, examining innovative concepts and emerging technological approaches from leading industry and academic institutions.
• The team of contractors will be producing deliverables as part of the NCIA project team that executes the Allied Command for Transformation (ACT) Programme of Work (PoW) 2026 for BMD. The academic partner will contribute to deliverables with his/her AI research expertise and scientific rigour to the feasibility study, ensuring state-of-the-art approaches are considered and responsibly evaluated.

Role Duties and Responsibilities

• Leading the identification and scoping of AI/ML use cases applicable to (T)BMD fire- control coordination and IAMD operational challenges.
• Reviewing existing NIAG SG300 reports and TBMD simulation data to establish baseline knowledge and identify relevant datasets.
• Designing a baseline data architecture and methodological framework for the responsible use of advanced AI technologies within the BMD domain, informed by NATO’s current AI architecture and near-term plans.
• Conducting feasibility studies on emerging AI/ML computational techniques (such as machine learning, deep learning, reinforcement learning, and large language models) and assessing their potential operational benefit for identified use cases.
• Developing and evaluating AI/ML software prototypes and demonstrators for selected BMD use cases at NATO UNCLASSIFIED level in the NATO Software Factory (NSF). All development to take place in NSF and to use existing LLMs where needed (to make it easier to migrate to NS).
• Preparing and providing subject matter (BMD AI) briefings, expert reports, and feasibility study documentation related to the project work.
• Developing and documenting a responsible AI framework aligned to the NATO Principles of Responsible AI Use (PRU), as outlined in the NATO AI Strategy, but specifically tailored to the BMD context. This work should follow NATO ambition and align with existing work on the topic as required.
• Planning and delivering subject matter (BMD AI) training and knowledge transfer sessions to the NCIA project team and a wider NCIA community, focusing on the BMD case. NCIA will share currently available AI training material with the contractor team.

Deliverables base period:

• D1- The team of contractors will deliver the following services and products for the BMD AI feasibility study (Year 1 – Feasibility and Architecture Definition phase):
  • Review of existing SG300 reports and TBMD simulation data, and identification of relevant datasets.
  • Scenario and use-case definitions (e.g. target characterisation, trajectory estimation, engagement coordination).
  • Feasibility Study Report v1 – documenting assessed AI/ML approaches and their applicability.
  • Kick-off and Review Meeting Minutes.
  • System Architecture Document – defining a baseline data architecture and methodological framework for responsible use of advanced AI technologies.
  • AI/ML algorithm evaluation and benchmarking report for identified use cases.
  • Software prototype – alpha release for at least one identified use case.
  • Progress briefing to NCIA project team.

• D2- The team of contractors will deliver the following services and products (prototype validation, knowledge transfer and roadmap phase):
  • Updated Feasibility Study Report v2 – incorporating findings from prototype testing.
  • Software prototype – beta release with demonstrated performance against identified scenarios.
  • Technical expert report documenting evaluation results and recommendations.
  • Supporting and participating in product demonstrations at relevant meetings and conferences.
  • Final Feasibility Study Report – with conclusions and recommendations for implementation at NATO CLASSIFIED level.
  • Technology Roadmap document for further development and integration of AI solutions in BMD capability.
  • Aligning to NCIA’s Principles of Responsible AI Use framework documentation, covering ethics, explainability, and governance considerations.

Deliverables option period 2027:

• D1- The team of contractors will deliver the following services and products:
  • Refined scenario and use-case specifications based on Year 1 findings.
  • Advanced software prototype – alpha release for expanded set of AI/ML use cases.
  • Review and update of data architecture framework.
  • Kick-off and review meeting minutes.
• D2- The team of contractors will deliver the following services and products:
  • Advanced software prototype – beta release with extended functionality.
  • Integration study report – assessing integration pathways into existing BMD BMC3I systems.
  • Validation and verification report for AI/ML models against operational scenarios.
• D3- The team of contractors will deliver the following services and products:
  • Updated software prototype with operator-in-the-loop testing results.
  • Operator feedback report and user acceptance assessment.
  • Participation in product demonstrations at relevant NATO meetings and conferences.
• D4- The team of contractors will deliver the following services and products:
  • Annual summary report on AI/ML prototype maturity and readiness for further NATO development.
  • Updated technology roadmap and recommendations for Year 3.
  • Training session on BMD AI developments and prototype demonstrations for NCIA project team.

Deliverables option period 2028:

• D1- The team of contractors will deliver the following services and products:
  • Final software demonstrator release – consolidated AI/ML solution package.
  • Comprehensive technical documentation for implementation teams.
  • Updated framework to show how responsible AI is addressed for these use cases, with lessons learned.
• D2- The team of contractors will deliver the following services and products:
  • Transition and integration support report for NCIA programme-level teams.
  • Final validation report against all identified operational use cases.
  • Publication-ready technical report for NATO scientific community.
• D3- The team of contractors will deliver the following services and products:
  • Transition and integration support report for NCIA programme-level teams.
  • Final validation report against all identified operational use cases.
  • Publication-ready technical report for NATO scientific community.
• D4- The team of contractors will deliver the following services and products:
  • Transition and integration support report for NCIA programme-level teams.
  • Final validation report against all identified operational use cases.
  • Publication-ready technical report for NATO scientific community.

Essential Skills, Experience and Certifications

• More than 10 years of professional experience in software engineering, AI/ML system development, or related technical disciplines, with demonstrated expertise in the full software development lifecycle.
• Hands-on expertise in AI/ML frameworks and tools, including but not limited to: PyTorch, TensorFlow, scikit-learn, or equivalent. Demonstrable experience implementing and evaluating ML models in production or research environments.
• Strong proficiency in software development using one or more of the following languages and frameworks: Python, Java, C#/.NET, JavaScript/TypeScript. Experience with backend frameworks such as Spring Boot, or equivalent enterprise-grade platforms.
• Experience in the design and development of distributed software architectures, including microservices, RESTful APIs, cloud-native components (AWS, Azure, or equivalent), and containerised deployment using Docker and Kubernetes.
• Experience in research project management, including contribution to research proposals, scientific studies, project planning, and reporting in a technical or academic context.
• Demonstrated ability to design, implement, and evaluate AI/ML software prototypes and demonstrators, including documentation of technical results in reports and specifications.
• Experience working in project teams with international composition, including both military and civilian stakeholders, and adapting to different organisational environments.
• Experience contributing to NIAG (NATO Industrial Advisory Group) studies or equivalent NATO science and technology activities.
• Candidate must have the nationality of one of the NATO nations.

Education

• University degree in Computer Science, Computer Engineering, Software Engineering, or a closely related field. A PhD in a relevant discipline is preferred.

Language

• Excellent command of spoken and written English.

Desired Skills, Experience and Certifications

• Experience with NATO C4ISR systems or defence-related software development, including familiarity with relevant standards and mission application architectures (e.g. BMC3I systems, JCHAT, XMPP, Link16, NVG).
• Experience with AI/ML use cases relevant to the defence domain, such as signal classification, anomaly detection, trajectory prediction, or multi-sensor data fusion.
• Experience with knowledge graph technologies, vector databases, semantic search, and retrieval-augmented generation (RAG) systems, particularly for large-scale data analysis applications.
• Familiarity with responsible AI principles, including model explainability techniques (SHAP, LIME, attention visualisation), fairness metrics, and AI governance frameworks.
• Experience with agile development methodologies and collaborative software development practices (e.g. Git, CI/CD pipelines, code review).
• Knowledge of NATO responsibilities and organisation, including ACT and ACO.
• Prior working experience in an international environment comprising both military and civilian elements.

Working Location

• Remote

Working Policy

• Offsite

Travel

• Some travel to other NATO sites may be required

Security Clearance

• Not required

At IMC, technology is not a department; it’s at the heart of everything we do. Developed in-house, our innovative software makes millions of trading decisions daily, and we win by making better and faster decisions than our competition. Our various Python Development teams create scalable business-critical applications, tools and automated tasks to equip IMC’s Amsterdam, London, Zug and Mumbai trading and business support teams.

Your Core Responsibilities

IMC uses a wide variety of technologies. Python is widely used across our tech stack, notably to make it more accessible to our traders, researchers and analysts. As a Python Software Engineer at IMC Amsterdam, you will take part in the development of high-performance trading systems, analytical tools, signals generation, backtesting frameworks, trade management UIs or regulatory surveillance & risk monitoring. Therefore your role will involve collaborating directly and closely with the business and other tech teams, from initially gathering requirements to short iteration cycles to refine the implementation, to integrate user feedback, to voice and implement your improvements and ideas.

All the software you’ll be building is for internal use only, there are no external clients/users and therefore no non-sense requests: the users of your work sit a few meters from you and you have control over the whole development cycle. Curiosity and interest into the trading and financial markets is a key success factor in this role, as you will be working directly on the business logic, in an environment where understanding the business context is primordial.

At IMC we believe in giving a lot of responsibilities quickly, you’ll be working on your goals with a high degree of independence, yet supported by a solid network of mentors, teammates and trading team highly motivated to make sure you are successful and share their passion for trading and technology. In this role, you might end up working on are a wide range of tasks - for example:

• Adding a new signal a trader came up with to our framework.
• Supporting a trading desk in building the data analytics and visualisation tools they need.
• Troubleshooting, debugging or deep diving into performance analysis.
• Teaching others how to use our tech in their own scripts.
• Building and distributing internal python libraries.
• Starting whole new full-stack projects from scratch.

Your Skills and Experience

• We care about 4+ years of working experience as a Software Engineer.
• Deep technical knowledge of Python, you understand its internal workings and know its strength and limitation. We use a range of Python libraries and frameworks (such as FastAPI (AsyncIO), APScheduler, Pandas, Polars, RQ, Pytest, Tensorflow, scipy, xgboost, scikit-learn, numpy, numba, celery).
• Experience with DevOps, databases, big data or frontend frameworks is a plus. (Some examples of tools we frequently work with: Kubernetes, Helm, Docker, Gitlab CI, TypeScript, React, Redis, PostgreSQL, Mongo, Dremio, Apache Iceberg, HDFS, Kafka, Apache Arrow/Parquet).
• A critical mindset and the ability to challenge ideas and solutions, to not take things for granted, validate assumptions and dig into the details to find and cover all corner cases. You’re not afraid to ask questions and to speak up.
• Experience working with financial data is a must, ideally in the financial services industry. The nature of this role requires both affinity and experience to this type of data.

Our tech stack includes an ever-evolving range of systems and technologies, and our engineers have the freedom to choose the best solution for the problem at hand. If a new technology has the potential to add value, we’ll actively invest in exploring, adopting and developing it. 

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

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 Amsterdam office or full remote from any part of Netherlands.

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 (45 min)
• 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

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.