The role

At Nebius, we’re building a next-generation AI compute platform for large-scale ML training and inference — from a few nodes to thousands of GPUs.
We’re looking for a Technical Product Manager to own product direction for Soperator — our Slurm-on-Kubernetes control plane for GPU clusters.
In this role, you will shape how ML engineers and research teams run, scale, and optimize distributed workloads in production.
If you care about systems that combine performance, reliability, and developer experience at the frontier of AI infrastructure, this role is for you.

Your responsibilities will include: 

• Own the full user journey across Soperator clusters: Slurm workflows, dashboards, alerts/notifications, node lifecycle, and training/inference capacity management.
• Define product direction end-to-end: problem discovery → solution design → delivery → adoption.
• Lead deep customer discovery through interviews, usage analytics, and workload analysis to uncover high-impact opportunities.
• Drive execution across platform teams: compute, networking, storage, observability, IAM and etc.
• Translate frontier ML and infrastructure ideas into practical product capabilities for real-world GPU clusters.
• Define success metrics, prioritize roadmap decisions with data, and ensure measurable customer/business impact.
• Lead the open-source strategy and execution for Soperator: shape public roadmap themes, prioritize OSS-facing capabilities, and ensure strong adoption in the community.  

We expect you to have: 

• 3–5+ years in Product Management, ML infrastructure/MLOps, distributed systems, or cloud platform engineering.
• Strong technical depth in distributed systems, cloud infrastructure, or ML platforms.
• Hands-on familiarity with large-scale ML training and orchestration tools (e.g., Slurm, Kubernetes, Ray).
• Track record of shipping technically complex products with multiple engineering teams.
• Strong communication and stakeholder management across engineering, research, and customers.
• Experience with product analytics, data-informed prioritization, and experimentation.
• High ownership, high learning velocity, and comfort operating in fast-moving AI infrastructure environments.

It will be an added bonus if you have: 

• Experience with GPU platforms and HPC primitives: InfiniBand/RDMA, topology-aware scheduling, high-throughput storage.
• Practical understanding of modern ML training stacks: PyTorch, DeepSpeed, FSDP/ZeRO, NCCL.
• Familiarity with efficiency and reliability metrics: Goodput, MFU, failure modes, preemption handling, health checks.
• Exposure to large-scale LLM training/inference systems.
• Experience in observability, performance tuning, or SRE/reliability engineering.
• Customer-facing technical experience (solutioning, support, architecture advisory).

About Nebius

Nebius AI is an AI cloud platform with one of the largest GPU capacities in Europe. Launched in November 2023, the Nebius AI platform provides high-end, training-optimized infrastructure for AI practitioners. As an NVIDIA preferred cloud service provider, Nebius AI offers a variety of NVIDIA GPUs for training and inference, as well as a set of tools for efficient multi-node training. 

Nebius AI owns a data center in Finland, built from the ground up by the company’s R&D team and showcasing our commitment to sustainability. The data center is home to ISEG, the most powerful commercially available supercomputer in Europe and the 16th most powerful globally (Top 500 list, November 2023).  

Nebius’s headquarters are in Amsterdam, Netherlands, with teams working out of R&D hubs across Europe and the Middle East. 

Nebius AI is built with the talent of more than 500 highly skilled engineers with a proven track record in developing sophisticated cloud and ML solutions and designing cutting-edge hardware. This allows all the layers of the Nebius AI cloud – from hardware to UI – to be built in-house, distictly differentiating Nebius AI from the majority of specialized clouds: Nebius customers get a true hyperscaler-cloud experience tailored for AI practitioners. We’re growing and expanding our products every day. 

Token Factory is a part of  Nebius Cloud, one of the world’s largest GPU clouds, running tens of thousands of GPUs. We are building an inference platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to deploy at massive scale. To deliver on that promise, we need an engineer who can make the platform behave flawlessly under extreme load and recover gracefully when the unexpected happens.

In this role you will own the reliability, performance, and observability of the entire inference stack. Your day starts with designing and refining telemetry pipelines — metrics, logs, and traces that turn hundreds of terabytes of signal into clear, actionable insight. From there you might tune Kubernetes autoscalers to squeeze more efficiency out of GPUs, craft Terraform modules that bake resilience into every new cluster, or harden our request-routing and retry logic so even transient failures go unnoticed by users. When incidents do arise, you’ll rely on the automation and runbooks you helped create to detect, isolate, and remediate problems in minutes, then drive the post-mortem culture that prevents recurrence. All of this effort points toward a single goal: scaling the platform smoothly while hitting aggressive cost and reliability targets.

Success in the role calls for deep fluency with Kubernetes, Prometheus, Grafana, Terraform, and the craft of infrastructure-as-code. You script comfortably in Python or Bash, understand the nuances of alert design and SLOs for high-throughput APIs, and have spent enough time in production to know how distributed back-ends fail in the real world. Experience shepherding GPU-heavy workloads — whether with vLLM, Triton, Ray, or another accelerator stack — will serve you well, as will a background in MLOps or model-hosting platforms. Above all, you care about building self-healing systems, thrive on debugging performance from kernel to application layer, and enjoy collaborating with software engineers to turn reliability into a feature users never have to think about.

If the idea of safeguarding the infrastructure that powers tomorrow’s multimodal AI energizes you, we’d love to hear your story.

As a Senior ML Engineer you will join our Static AI team which develops state-of-the-art ML models that operate on millions of machines worldwide, making sub-second decisions to stop malware before it runs. We bring together cross-functional skill sets, including data science, ML engineering, and software engineering, to research, develop, ship, and support ML malware detection engines across Windows, Mac, and Linux machines and cloud environments.

What Will You Do?

Primary responsibilities include: 

• Data pipeline architecture: Design, build, and maintain scalable data pipelines to handle millions of data points from internal and external sources
• ML infrastructure: Develop and optimize the infrastructure required for training, evaluating, and deploying ML models at scale
• Cross-functional Collaboration: Work closely with Data Scientists and Software Engineers to translate research into production-ready detection engines
• System optimization: Build and improve analytics systems that learn from a wide variety of data sources to enhance our existing ML models
• Production support: Ensure the reliability and performance of ML malware detection engines across Windows, Mac, Linux, and cloud environments
• "Production experience" - good level of coding, tests, dealing with CI
• You will also be encouraged to write white papers, blogs, and articles or present in meetups / conferences (but only if you wish to)

What Skills and Knowledge Will You Bring?

Ideal candidates will have: 

• Degree in Computer Science or Mathematics, or equivalent experience
• Experience with collecting and building large datasets for training machine learning models
• Producing, deploying and running ML in production environments
• Experience working with Big data technologies, such as Spark/Databricks, Hadoop, and others 
• Ability to work collaboratively to translate business requirements into technical solutions
• Willingness and ability to work in a diverse, globally distributed team
• The ability to work on difficult problems independently
• Strong communication skills. Our team relies on strong communication, since we have a diverse and sometimes exclusive set of skills, yet our end product relies on all team members. Thus, we need our team members to be able to communicate with people outside our team (with a wide range of backgrounds/positions, product/support/engineering, …)
• A fiery determination and a strong team-player mindset - the team's defining trait is an 'all hands on deck' attitude

Experience in some of these areas is an advantage

• Experience in the cyber domain - security products, especially detection systems
• Cloud infrastructure (AWS) / MLOps experience
• Experience working with Infrastructure as code (IaC)
• AI tooling experience - We are an AI-oriented engineering team. We believe in automating the mundane so our developers can focus on high-level architecture and complex problem-solving. We provide the latest enterprise AI tools and encourage a culture of 'working smarter, not harder.

Why SentinelOne?

AI is redefining how the world operates and rewriting the rules of security in real time, and SentinelOne was built for this moment. From day one, we architected an AI-native platform designed to operate at machine speed, not as an add-on to legacy systems but as the foundation itself. If you want to build where innovation and impact move together, this is that place.

We invest in our Sentinels with comprehensive, competitive benefits designed to support you and your family:

• Enjoy flexible hybrid work in Prague (Karlin), Prague-based employees are required to work from the office at least two days per week.
• Stock & Bonuses: Grant of Restricted Stock Units with a 4-year vesting plan, annual performance-based bonuses, and an employee stock purchase plan.
• Time Off & Well-being: Flexible Time Off, on top of the standard 5 weeks vacation, flexible paid sick days, fully paid Short Term Sick/Nursing Leave, 16-week parental leave, grandparent leave, and additional company holidays.
• Insurance & Health: Pension Insurance Contribution, Premium life insurance, Private medical care (for you and +1), and a Global Employee Assistance Program.
• Work Perks: Monthly meal and well-being allowance, high-end MacBook/Windows laptop, work-from-home support, and in-office refreshments.
• Growth & Community: LinkedIn Learning, internal mentoring, educational support, generous referral bonuses, and optional company events (sports, BBQs, charity).

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

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

Joining Collibra’s Artificial Intelligence team

We are looking for an experienced AI Engineer who will design and deliver Artificial Intelligence solutions that improve our customers' experience and enable informed, data-driven decision making. We seek someone excited about working with cutting-edge technologies to build pragmatic, performant AI solutions. You'll collaborate with a team of outstanding builders around the world, characterized by a love for innovative technology, a desire to solve problems, and a continuous pursuit of building simple, elegant products that delight customers and simplify all levels of data engagement. You will work primarily with Data Scientists, other AI Engineers, and Machine Learning Operations Engineers, as well as teams across Collibra.

This is a hybrid role based in our Prague office. Our hybrid model means you’ll work from the office at least two days each week. This setup helps us stay connected, work more closely together, and keep making progress as a team.

AI Engineers at Collibra are responsible for

• Monitoring of the larger AI/ML landscape, including emerging techniques and current state-of-the-art approaches 
• Transforming prototypes into pragmatic, production-ready solutions which solve impactful business problems
• Creating, operationalizing, and supporting AI/ML models and visualizations to power actionable value and insights
• Evaluating algorithmic results for quality, adaptability, and efficiency
• Contributing to evolving our platforms for Artificial Intelligence and Machine Learning capabilities in support of Collibra’s data products
• Collaborating with cross-functional teams and mentoring junior engineers on AI best practices

You have

• 5+ years of experience delivering production-grade AI/ML projects related to business functions, incorporating advanced techniques (e.g., LLM-based applications, RAG systems, NLP, neural networks, recommendation engines)
• Experience with SQL and relational databases, query authoring as well as working familiarity with a variety of databases (e.g., time series, graph, vector)
• Built and deployed production code in Python with associated AI modules (LangChain, LangGraph, Pandas, Numpy, Tensorflow, etc.)
• Experience with model evaluation, testing frameworks, and validation methodologies
• You must have work authorization to work in Prague, Czech Republic
• A bachelor’s degree or equivalent related working experience is required

You are

• Deeply technical, driven, and analytical about the insights data can deliver
• Able to cut through hype and noise to identify foundational problems and build maintainable, performant solution
• Passionate about the mathematical principles powering the Generative AI revolution and how to drive sustainable value from LLMs and other GenAI technologies
• Experienced supporting diverse stakeholders and working with cross-functional teams in dynamic environments
• A clear communicator who can explain complex technical concepts to both technical and non-technical audiences
• Product-minded with empathy for end users—you consider how people will actually interact with AI features, not just technical sophistication
• Eager to both learn from others and share knowledge ; AI is evolving rapidly and no one has all the answers

Reporting to Collibra’s Senior Manager of AI Engineering, measures of success are

• Within your first month, you will familiarize yourself with the existing data platform and data products by diving into the architecture, existing products, and the Collibra platform ecosystem
• Within your third month, you will start to deliver new AI/ML features into our SaaS platform
• Within your sixth month, you will operationalize AI products into production environments and contribute to our AI roadmap

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

Your key skills and capabilities:

·      Implementing cloud-native data platforms
·      Engineering scalable and reliable pipelines
·      Good knowledge of distributed computing with Spark
·      Understanding of cloud architecture principles and best practices
·      Hands-on experience in designing, deploying, and managing cloud resources
·      Excellent python and SQL skills
·      Agile ways of working
·      Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform
·      Monitoring and performance tuning of cloud-based applications and services

Nice to haves: (MLOps):  

• Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance. 
•  Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining 
• ML Pipelines & CI/CD – Automate end-to-end ML workflows 

We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform.