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

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

THE ROLE

As a Data Engineer - Agentic AI focus you will architect the high-performance data ecosystem that transforms raw information into actionable intelligence and agentic AI solutions. You will bridge the gap between complex engineering and business impact, collaborating with Data Scientists and Product Managers to build the infrastructure that scales our AI capabilities. This isn't just about moving data; it’s about engineering the foundation for the next generation of automated customer support and operational excellence.

WHAT YOU'LL DO

• Architect Scalable Data Infrastructure: Design, deploy, and optimize production-grade ETL pipelines and data models that ensure high-quality, AI-ready datasets for global business intelligence.
• Drive AI Evolution: Lead the technical preparation and prototyping of AI-driven workflows, integrating emerging LLM frameworks to automate complex business processes and enhance decision-making.
• Ensure Data Excellence and Integrity: Own the end-to-end data management lifecycle, implementing robust governance, security, and monitoring to maintain a "single source of truth" for critical stakeholders.
• Collaborate and Solve Across Functions: Partner with cross-functional teams to translate business needs into technical specifications, serving as the bridge that turns raw data into measurable organizational impact.
• Operational Reliability: Manage business-hour on-call rotations to ensure the seamless performance of ingestion systems, maintaining high availability for our global data consumers.

WHAT YOU BRING

• Advanced Engineering Proficiency: Deep technical expertise in SQL, Python, and data modeling, with a proven track record of maintaining complex data pipelines and workflow orchestration tools like Airflow or DBT.
• Modern Data Stack Expertise: Hands-on experience managing relational and NoSQL environments (such as Snowflake, PostgreSQL, or MongoDB) within AWS cloud infrastructures.
• Growth Mindset & AI Curiosity: A strong desire to apply traditional data engineering principles to intelligent automation, including the ability to adapt to new technologies like LLM orchestration and model evaluation.
• Collaborative Communication: The ability to distill complex technical concepts into clear insights for non-technical partners, ensuring alignment across global time zones and diverse teams.
• We are primarily an in-office environment and therefore, you will be expected to work from the Prague, Czech Republic office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.

#LIONSITE

THE ROLE

As a Software Systems Architect, you will serve as the technical visionary and bridge between complex global business needs and high-quality technical execution. Based in our Prague R&D, you will define the architectural roadmap for engineering teams across Prague and Santa Clara, transforming requirements from Sales, Marketing, HR, and Customer Service into scalable, AWS-native solutions. Your mission is to elevate our engineering standards by architecting cutting-edge Agentic systems and intelligent orchestration layers. This is a high-impact leadership role where you will drive innovation, mentor a growing organization of 30+ developers, and ensure our internal platforms are resilient, secure, and future-proof.

WHAT YOU'LL DO

• Architectural Governance & Design: Lead the end-to-end design of complex, distributed systems by creating high-fidelity blueprints that align technical strategy with internal business goals for scalability and maintainability.
• Hands-on Technical Leadership: Drive "lead-by-example" execution by building Proof of Concepts (POCs), conducting live technical demonstrations, and authoring the gold-standard design documentation that sets the bar for the entire engineering organization.
• Systemic Quality Assurance: Act as the final authority on technical designs, rigorously reviewing architectural proposals to mitigate risks in performance, reliability, and security before they reach production.
• Agentic Systems Innovation: Drive the adoption of Agentic services and prediction models, architecting the orchestration layers that enable AI agents to solve intricate business problems for global stakeholders.
• Technical Mentorship & Culture: Actively coach a community of 30+ developers to improve the collective mastery of design-led development, advanced unit/functional testing practices, and modern coding standards.
• Strategic Collaboration: Partner closely with the Head of Engineering in Santa Clara to translate high-level global roadmaps into actionable technical strategies, ensuring seamless execution across geographic boundaries.
• Operational Excellence: Standardize the integration of complex data environments, ensuring that our architectural patterns support robust data engineering and AI-driven workflows at scale.

WHAT YOU BRING

• Software Engineering Mastery: 10+ years of professional experience in software development and architecture, including  hands-on coding skills in Python (preferred) or Java.
• Technical Influence: A proven "Architect-Coder" mindset with the ability to influence engineering culture through technical excellence, high-quality documentation, and a "quality-first" approach to testing.
• Cloud Architecture Expertise: Deep, hands-on experience architecting resilient microservices and distributed systems within AWS (specifically utilizing SQS, SNS, and SageMaker).
• Agentic AI Proficiency: A sophisticated understanding of Agentic AI architecture, including multi-agent orchestration, LLM-powered tool calling, and the integration of prediction models into business workflows.
• Complex Problem Solving: A track record of resolving highly complex technical challenges involving large-scale data engineering, enterprise integration, or AI-driven automation.
• Enterprise Tooling Integration: Professional familiarity with modern data and integration platforms such as Snowflake, dbt, Fivetran, SnapLogic, Boomi, or Reltio.
• In-Office Collaboration: We are primarily an in-office environment and therefore, you will be expected to work from the Prague office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.

 #LI-ONSITE

The role

This role is for Nebius AI R&D, a team focused on applied research and the development of AI-heavy products. Examples of applied research that we have recently published include:

• investigating how test-time guided search can be used to build more powerful agents;
• dramatically scaling task data collection to power reinforcement learning for SWE agents;
• maximizing efficiency of LLM training on agentic trajectories.

One example of an AI product that we are deeply involved in is Nebius Token Factory — an inference and fine-tuning platform for AI models.

This role will require expertise in distributed systems to build large-scale LLM training platform.

Your responsibilities will include: 

• Designing and developing LLM training platform.
• Maintaining our ML infrastructure, ensuring optimal performance, scalability and reliability.
• Improving job scheduling strategies to minimize resource fragmentation. 

We expect you to have:

• 5+ years of professional software development experience.
• Strong software engineering skills (we mostly use Python and Go).
• Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing.
• Experience with developing web services.
• A commitment to maintaining extreme rigor in all job-related activities.

Nice to have:

• Previous experience working with language models or other similar NLP technologies.
• A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.
• Strong engineering skills, including experience in developing large distributed systems or high-load web services.
• Open-source projects that showcase your engineering prowess.

The role

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 & fine-tuning platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to train & deploy at massive scale.

• Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency.

• Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation.                                                                                
• Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware

We expect you to have:

• A profound understanding of theoretical foundations of machine learning and reinforcement learning.

• Deep expertise in modern deep learning for language processing and generation

• Experience with training large models on multiple computational nodes

• Reasonable understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)

• Strong software engineering skills (we mostly use Python)

• Deep experience with modern deep learning frameworks (we use JAX)

• Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing

• Strong communication and leadership abilities

Nice to have:

• Previous experience working with language models or other similar NLP technologies.

• Familiarity with important ideas in LLM space, such as MHA, RoPE, ZeRO/FSDP, Flash Attention, quantization

• A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.

• Strong engineering skills, including experience in developing large distributed systems or high-load web services.

• Open-source projects that showcase your engineering prowess

• Excellent command of the English language, alongside superior writing, articulation, and communication skills.

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. 

The role

We’re looking for a Software Engineer with strong C++ expertise to join the team building and operating Nebius Data Platform — a distributed storage and a processing platform that acts as the company’s “source of truth” and the backbone of many internal (and some external) products.

Nebius Data Platform is a single multi-tenant ecosystem based on YTsaurus — instead of running separate HDFS/Kafka/HBase-style systems, we provide storage, compute, and analytics capabilities inside one platform.

Built on top of the open-source YTsaurus ecosystem, we run and extend our own Nebius distribution and develop significant in-house functionality (core and platform-level). We can design, implement, and roll out features end-to-end on our clusters without waiting for upstream approvals and contribute upstream when it makes sense.

At scale today, this includes~500 servers, ~20k CPU cores and ~10 PB of compressed data in our largest production cluster, supporting workloads ranging from business-critical pipelines and financial transactions to large-scale ML/LLM training datasets and compute.

What’s inside the platform

You’ll work on a system that includes (and ties together):

• Distributed Storage (Cypress): transactional semantics, tiered storage, erasure coding, replication, and strong reliability expectations.
• Compute & ETL: a cluster-wide job scheduler (tens of thousands of cores), MapReduce, YQL for SQL-like data processing, and SPYT (Spark over YTsaurus) for modern data engineering.
• Interactive analytics (CHYT): ClickHouse® instances spun up directly on compute nodes for fast SQL over data in-place.
• Dynamic Tables: low-latency NoSQL KV with distributed ACID transactions for OLTP-style workloads and feature stores.
• Orchestracto: workflow orchestration deeply integrated with the platform (Airflow-like, but platform-native).

What you’ll do

We’re looking for engineers who combine strong systems skills with product sense: understanding who uses the platform, why certain capabilities matter, and making pragmatic trade-offs to maximize impact. On our team, engineering work is expected to be connected to real users and outcomes — you’ll regularly align with internal stakeholders, clarify requirements, and help drive prioritization.

In this role, you will:

• Design and implement new functionality in YTsaurus core (C++) with production reliability in mind.
• Build and evolve platform-level capabilities: platform architecture and operating model—multi-cluster growth, shared primitives, and a consistent experience that scales with new teams and use cases.
• Improve end-to-end platform experience for internal (and external-facing) users: APIs, guardrails, debugging workflows, and automation.
• Own production quality: incident response / on-call rotation, root cause analysis, and turning learnings into durable fixes.

Example projects

• Roll out sharded YTsaurus masters (incl. Kubernetes operator support) and build automatic balancing of metadata across master cells (consensus groups) to remove control-plane bottlenecks and unlock 10–100x cluster growth.
• Make CHYT interactive SQL faster and more predictable at high load via performance work like data-skipping / min-max-style indexes and improved execution introspection.
• Turn Orchestracto into a platform product by defining the building blocks, developer experience, and governance for how teams create and share workflows.
• Scale and harden Parquet-on-S3 for native YTsaurus workloads by tackling replication/movement, consistent lifecycle semantics, and master-server metadata optimizations for performance and reliability.
• Design and ship complete, trustworthy audit trails for data changes (who/what/when) across heterogeneous storage and compute paths.

Tech stack

• Core: modern C++ (C++20, async + multithreaded primitives)
• Services & tooling: Go and Python (microservices, utilities, integration tests)

What we expect

• 5+ years of software engineering experience.
• Strong C++ skills (you’ll write core code).
• Working knowledge of Python and/or Go (you don’t have to be expert, but should be comfortable navigating them).
• Experience developing and/or operating high-load, distributed services.
• Production mindset: ability to use SSH, read logs/metrics/traces, and debug distributed systems behavior.
• Solid CS fundamentals: algorithms, data structures, concurrency basics.

Nice to have

• Experience with Big Data systems (YTsaurus/Hadoop/Spark/ClickHouse/Kafka-like ecosystems).
• Experience with multi-tenant platforms, schedulers, resource isolation, quotas, and reliability engineering.
• Strong performance engineering skills (profiling, lock contention, latency/throughput tradeoffs).

We conduct coding interviews as part of the process.

The role

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 a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.

Some directions we are currently working on, and which you can be a part of:

• Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
• Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
• Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We expect you to have:

• A profound understanding of theoretical foundations of machine learning and transformer architecture.
• Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools
• Understanding of GPU memory hierarchy and compute/memory tradeoffs
• Familiarity with important ideas in LLM space, such as MHA, RoPE, KV-cache, Flash Attention, and quantisation
•  Understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
•  Strong software engineering skills (we mostly use Python)
• Deep experience with modern deep learning frameworks
• Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
• Strong communication and leadership abilities

Nice to have:

• Experience working with open-source inference engines (vLLM, SGLang, TensorRT-LLM), including contributions
• Experience with kernel languages or DSLs such as Triton, Cute, CUTLASS, CUDA
• A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.
• Strong engineering skills, including experience in developing large distributed systems or high-load web services.
• Open-source projects that showcase your engineering prowess
•  Excellent command of the English language, alongside superior writing, articulation, and communication skills.

The role 

Nebius Token Factory is a next-generation platform for LLM inference and deployment. It gives companies and developers access to dozens of state-of-the-art open-source models (LLMs, Vision, Embeddings, Image Generation) with enterprise-grade guarantees, including private endpoints, zero-retention data flow, transparent pricing, and easy scaling without GPU ops overhead.

Token Factory is part of Nebius, a company building next-generation cloud infrastructure for the global AI economy, helping teams solve real-world problems and scale AI without massive infrastructure costs or large in-house ML teams.

We are looking for a strong Product Designer  who will help turn complex AI infrastructure into a clear, controllable, and thoughtfully designed product for a professional audience. In this role, you’ll work on one of the most technically advanced AI products on the market, immerse yourself in modern LLM and AI infrastructure operating at production scale, and have real influence on UX and product decisions across core user scenarios.

You’re welcome to work in our offices in Amsterdam, Berlin, London or Prague with a hybrid work schedule.

Your responsibilities will include: 

• Designing clear, scalable interfaces for core end-to-end AI workflows: model selection and comparison, deployment and inference, inference observability and analysis, fine-tuning, dataset management, batch inference, and agent configuration.
• Own the end-to-end design of services and features within the Nebius Token Factory, from discovery to delivery.
• Being ready to write high-quality interface copy — clear, precise text that helps users navigate complex scenarios and make informed decisions.
• Developing a deep understanding of how AI systems work under the hood, translating complex technical processes (fine-tuning, data pipelines, agent workflows) into clear and manageable user interfaces.
• Collaborating with product managers to formulate hypotheses, validate them with data and user feedback, and improve key product scenarios.
• Using modern AI tools as part of the design process across research, ideation, and prototyping stages (Figma Make, Cursor, lovable.dev, ChatGPT, Claude, etc.).
• Working closely with product, engineering, and ML teams — discussing, revisiting, and improving solutions based on feedback and shared understanding.
• Clearly present design solutions and rationale to cross-functional teams
• Contribute to the development and maintenance of the design system, ensuring consistency and quality across the product  

We expect you to have: 

• 5+ years of experience in product teams (SaaS / B2B / DevTools experience is a strong plus). Strong product thinking and the ability to turn complex processes into clear and transparent user journeys.
• Experience working on technically complex products where understanding system logic and processes matters as much as UI. Ability to balance usability, consistency, and innovation in complex systems.
• Willingness to dive into the AI domain: how datasets are prepared for fine-tuning, how fine-tuning works, how agents and their workflows are structured.
• Openness to collaboration: ability to listen, argue constructively, and incorporate team feedback.
• Interest in UX research and a clear understanding of its value for product decisions.

It will be an added bonus if you have: 

• Experience with AI products, ML platforms, or developer tools.
• Understanding of API-first products.
• Experience designing enterprise-grade interfaces.
• Interest in product architecture and how systems work “under the hood.”

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.

A resident of JetBrains' startup incubator, Spectrum enjoys startup speed and autonomy, and is backed by 25 years of developer tooling expertise. We are looking for a Senior ML Researcher to develop the core methods that make Spectrum possible – novel approaches to temporal ontology extraction, contradiction detection, and semantic alignment across heterogeneous software artifacts. You will help define and execute the research agenda, while also collaborating with JetBrains Research and external academic advisors.

Your responsibilities will include:

• Developing methods for LLM-based ontology construction from code, documentation, and issue trackers, including contradiction detection and reasoning over the resulting knowledge graph.
• Creating datasets, metrics, and benchmarks that drive measurable improvements across the system.
• Prototyping and validating research ideas, then collaborating with ML and software engineers to bring them to production.
• Collaborating with JetBrains Research and external academic advisors to shape our research agenda.
• Publishing research findings at top venues and representing Spectrum in the research community.

We expect you to have:

• A PhD (or equivalent research experience) in NLP, knowledge graphs, ontology learning, information extraction, or a closely related field.
• A strong publication record in at least one of the following areas: knowledge graph construction, ontology learning, information extraction, or NLP.
• Experience applying LLMs to structured knowledge extraction tasks.
• Experience designing and running rigorous experiments, including hypothesis formulation, ablation studies, and statistical evaluation.
• Strong Python and PyTorch skills, with the ability to implement research ideas from scratch.
• Excellent communication skills, with the ability to explain complex technical concepts to diverse audiences.
• Proficiency in English, both written and verbal.

Our ideal candidate would also have:

• Experience with ontology engineering, semantic web technologies (OWL, RDF, SPARQL), or ontology alignment.
• A background in code analysis, developer tools, or software engineering research.
• Experience with knowledge graph embedding methods or graph neural networks.
• Experience in early-stage startups – you enjoy the zero-to-one phase.
• Actively contributed to relevant open-source projects.

What we offer:

• A competitive salary and JetBrains benefits.
• A generous runway and corporate resources with startup autonomy.
• Full support for publishing ML research and attending industry conferences.
• Collaboration with JetBrains Research and external academic advisors in the knowledge graph and ontology learning community.
• A motivated team that moves fast, with short feedback loops, rapid prototyping, and lots of ownership.
• A rare applied research role where your methods ship in a real product used by software teams.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI

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

The Python Ecosystem team builds PyCharm – one of the most popular Python IDEs in the world – along with the Python plugin for IntelliJ IDEA. As AI changes how developers write, debug, and ship code, we’re making our Python tools AI-native. We’re looking for an AI Lead to drive this effort by shaping the architecture, building key components hands-on, and guiding the team in making strong decisions around AI-powered product development.

In this role, you will:

• Lead the technical design of AI features in PyCharm and our Python tooling, including MCP integrations, agent workflows, tool design, and context management.
• Prototype and ship features yourself – this is a hands-on technical leadership role.
• Help the team adopt AI technologies effectively by providing technical guidance, reviewing approaches, and sharing practical patterns.
• Evaluate emerging AI tools, frameworks, and providers, and make pragmatic decisions about what to build and what to integrate.
• Work closely with product managers, ML engineers, QA specialists, and senior developers to turn product ideas into solid technical plans.
• Navigate a large Kotlin/Java codebase and expose IntelliJ Platform capabilities – such as code analysis, inspections, refactorings, and type inference – to AI-powered workflows.
• Contribute to cross-team AI efforts and help define shared engineering patterns where appropriate.

We’d love to talk to you if you have:

• Strong software engineering experience, particularly in Python and/or Kotlin/JVM.
• Deep, hands-on familiarity with modern AI tooling for developers, including coding assistants, agents, MCP, and LLM-based workflows.
• Experience working in large, mature codebases and delivering meaningful technical change without compromising quality.
• Familiarity with LLM evaluation and benchmarking.
• A track record of making good technical decisions in fast-moving, ambiguous areas like proposing architectures, building prototypes, and iterating based on feedback.
• Strong communication skills and the ability to influence technical direction without formal authority.
• A strong product sense and care about solving real user problems, not just building interesting technology.

Nice to have:

• Experience with IDE or developer tooling internals, such as language servers, code analysis, or related systems.
• Experience with the Python AI/ML ecosystem.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Relocation support. We help make your move as smooth and stress-free as possible. (keep if relevant for the role)
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#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

We’re building a new product from scratch - focused on pragmatic, production-ready LLM-based agents.
The research and architecture phase is happening. Now we need engineers who will help turn those decisions into a real, scalable system.
This is not about blindly playing with prompts.
This is about building backend services, APIs, evaluation pipelines, and infrastructure that make agentic systems reliable in production.
We’re tackling a real-world problem with a clear path to market, backed by SafeQ Cloud - and you’ll also help shape and improve the in-house tools that power the product.

You won’t just get tickets — you’ll be part of discussions, refinements, and decisions.

If you’d like to work remotely, no problem. We’re used to hybrid collaboration based on team agreement.

WHAT WE OFFER:

• Things to be fresh. We’re building new AI products just now - and you can be part of the journey from start to finish. You’ll work across a wide range of AI disciplines, from text processing through image analysis to process automation
• No coding monkeys. We build solutions, not just follow specs.
• Work from idea to delivery.
• A manager-less structure. No hierarchy, everyone has a say.
• A team that decides. Hiring, promotions, direction - all in the team.
• No bulls**t KPIs.
• Learning and knowledge sharing is part of the job.

WHAT WE EXPECT FROM YOU:

• TypeScript (Node.js)
• Ideally React (or willingness to learn)
• Experience with building real applications (not just tutorials)
• Attention to quality (code reviews, testing, best practices)
• Ability to work across backend and frontend
• Be open-minded
• Come up with improvements and realize them
• Share your knowledge with others
• Readiness to learn new things

NICE TO HAVE:

• DevOps / SRE basics
• QA mindset
• Security awareness
• UX / product sensitivity
• Interest in AI / LLMs (not required, but we’ll expose you to it)

One of our teams is looking for a new teammate. They are focusing on bringing AI into real products - not as a toy, but as something that actually solves problems.

You’ll be working on practical use of LLMs and AI approaches in a product environment. That means experimenting, validating ideas, and helping teams understand what works and what doesn’t. You won’t be sitting in a research silo - you’ll be working directly with teams and helping turn ideas into something usable.

If you’d like to work remotely, no problem. We’re used to hybrid collaboration based on team agreement.

WHAT WE OFFER:

• Things to be fresh. We’re building new AI products just now - and you can be part of the journey from start to finish. You’ll work across a wide range of AI disciplines, from text processing through image analysis to process automation
• No coding monkeys. We experiment, question, and figure things out.
• Work that starts with an idea and ends in a real product.
• A manager-less structure. No hierarchy, everyone has a say.
• A team that decides. Hiring, promotions, direction - all in the team.
• No bulls**t KPIs.
• Learning and knowledge sharing is part of the job, not a side activity.

WHAT WE EXPECT FROM YOU:

• Python
• Experience with at least one AI / ML-related project (school, personal, or work)
• Basic understanding of:
  • datasets
  • statistics
  • good practices in data/ML work
• Interest in AI beyond just LLMs
• Ability to explain things and share knowledge with others
• Be open-minded
• Come up with improvements and realize them
• Share your knowledge with others
• Readiness to learn new things

NICE TO HAVE:

• Experience with LLMs (RAG, agents, fine-tuning…)
• Experience with AI frameworks or tools
• Any exposure to production systems or cloud

We are seeking a highly motivated and experienced AI Product Manager to join our rapidly expanding product and engineering organization. Embedded within a team dedicated to our AI capabilities, you will serve as the team's product manager, championing the product AI strategy, driving rigorous prioritization, and ensuring successful execution through rapid iteration and shipping.

The ideal candidate for this role would possess a strong, hands-on understanding of the evolving AI landscape and the Low- and No-Code market, with a bias for action and building. Success in this role implies owning and articulating a compelling product vision to executive stakeholders, driving alignment across the organization, and deep collaboration with AI researchers, engineers, data scientists, and go-to-market teams.

What you’ll do:

• Identify, analyze, and prioritize customer and market opportunities specifically related to leveraging our AI capabilities, aligning these with our overarching product vision. Collaborate closely with customers to bring innovative AI-driven solutions to market.
• Own and articulate a compelling product vision to executive stakeholders, driving alignment across the organization.
• Serve as the subject matter expert and evangelist for your assigned AI product area, showcasing its potential, benefits and thrive for an outstanding user experience.
• Continuously engage with users, customers, partners, researchers, and engineers to deeply understand their needs and deliver exceptional AI product solutions that address real-world challenges.
• Drive the rapid ideation, prototyping, and launch of new AI-driven product features (e.g., early-stage tools and projects). Shipping high-impact features is the core success metric.
• Collaborate with the development team to triage and prioritize defects, ensuring the quality and reliability of our AI capabilities.
• Represent the voice of the customer and advocate for AI-driven solutions in daily scrums and other software development ceremonies.

What we are looking for:

• A "builder" mindset with a proven history of shipping high-impact, innovative products and prototypes, particularly in early project stages. Demonstrated ability to go hands-on with technical execution (e.g., prototyping, light coding).
• A Driver who can confidently align executive and cross-functional stakeholders on strategic vision and execution. Be the CEO of your product.
• Strong understanding of best practices in product management, agile development methodologies, and SaaS business models, with a specific understanding of the unique considerations for AI product development.
• Entrepreneurial thinking, Proactive and collaborative individuals with exceptional communication skills, capable of articulating complex AI concepts to both technical and non-technical audiences.
• 1 to 3 years of experience in a product management role, ideally with some exposure to or focus on AI/ML products or features, with a good understanding of LangChain or similar LLM Frameworks.
• A deep focus on and passion for cutting-edge developer tools and models (e.g., Claude Code, Cursor, LlamaIndex, etc.).
• 3-5 years of professional experience in a customer-facing technical role, demonstrating an ability to understand and translate customer needs into technical solutions.
• Bachelor's degree in computer science, information systems, business, or a related field with a strong technical aptitude and interest in artificial intelligence.
• Excellent written and verbal fluency in English.
• Proven ability to communicate and understand complex technical concepts, particularly in the realm of AI/ML, and translate them into clear business value propositions.
• Experience as an advanced user of automation (Low and NoCode), integration, SaaS APIs, and/or no-code software solutions is a plus. Familiarity with AI/ML platforms and Frameworks is a must, tools, and concepts (e.g., model deployment, data pipelines, ethical AI considerations) is a significant advantage.

🚀 Interview Process

Here’s what you we can expect when you apply:

 Step 1: Pre-Application Video Submission. Briefly walk us through something you vibe-coded recently - can be for work, or a personal project. An idea, prototype, SaaS or an app will work. We'd love to hear about your thought process in lovable, v0, cursor or your vibe‑coding tool of your choice. Screen‑record a very short video walkthrough of the user experience, and explain how you iterated and what you learned. This helps us get an understanding of your product development process. No polish needed – just your thinking, tools, and approach in action.

After you’ve submitted your task and CV, we’ll take a look and be in touch soon with next steps.

What we offer:

• 📈 RSUs grant in a rapidly growing company raising its value every day
• 💸 Annual bonus
• 🌎 Multinational team with 42 nationalities creating the future of automation
• 🎓 Learning & Development plan (online language, professional courses, conference tickets and other trainings) & 2 Company Learning Days per year 
• 🌳 1 Company Impact Days per year
• 🍎 Notebook/Macbook and 34’’ curved monitor
• 🏝 25 days of vacation, 4 sick days, company day off on 31st December
• 🫶 10 care days to care for your loved ones
• 👨‍👩‍👧‍👦 Extra parental vacation (3-6 months) 
• 👶 RSUs grant for a newborn child
• ☂️ Life insurance
• 🏋️ Benefit Plus Cafeteria (incl. MultiSport Card)
• 💰 Remote working allowance
• 🍍 Snack bar, coffee, tea, fruit and vegetable, and sweets all day - every day - available for everyone
• 🥗Wednesday lunch
• 🏡 Flexible working hours + home office 
• 🐕 Company therapy pets in Prague's office (dog-friendly office)
• 🖨 Company 3D printer 
• 🥳 Team buildings, parties, and company events multiple times a year

#careeratmake

#LI-KN1

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 and free developers to focus on creativity and problem-solving.

The IntelliJ AI team develops the AI-specific core of JetBrains IDEs. We work on agentic workflows, intelligent editing assistance, and new AI-powered capabilities that redefine how developers interact with the IDEs.

We are looking for a Senior Software Developer to help us build the next generation of AI-powered features in JetBrains IDEs.

In this role, you will:

• Integrate state-of-the-art AI agents into our IDEs, continuously improving the developer experience of working with them.
• Enhance agent capabilities by equipping them with the most intelligent tools available in our IDEs.
• Create novel AI features with no direct market analogues, experimenting with and exploring the new possibilities brought by modern AI technologies.
• Measure and evaluate the performance of agents and other AI features to achieve reliability and reproducibility.

We are looking for engineers who:

• Want to help build the best IDEs in the world and push the boundaries of developer tools.
• Have strong knowledge of Java, Kotlin, or similar languages.
• Are comfortable designing and developing complex software systems.
• Are self-driven and able to take end-to-end ownership of challenging features.
• Possess strong communication skills in English, both written and verbal.

Experience that would be especially valuable:

• Building AI-powered features, especially with LLM-based systems.
• Developing IDEs, code editors, or complex desktop applications.
• Working on performance-sensitive or correctness-critical systems.
• Concurrent programming and performance optimization.

Why join JetBrains?

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family.
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Relocation support. We help make your move as smooth and stress-free as possible.
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.
  *Some benefits may vary depending on location.

#LI-IM1

Kineto is a next-generation platform that enables creators, educators, and small businesses to generate, deploy, and operate fully functional AI-powered web applications – instantly and at scale. It combines LLM-driven code generation, multi-tenant Postgres (Neon), dynamic hosting (GKE and Knative), automated deployments (Flux), analytics, billing, and a seamless chat-based UX to make software creation accessible to everyone. Our team is growing rapidly, and we’re now seeking an experienced Infrastructure Engineer who can design, build, and maintain our cloud-native platform, with a focus on scalability, reliability, and automated operations.

What you’ll do:

Cloud and platform engineering (DevOps):

• Design, implement, and manage the core infrastructure powering Kineto's platform on Google Cloud Platform (GCP), including networking, security, and identity management.
• Build and operate resilient, highly available distributed systems using Kubernetes (GKE), Knative, Istio, and related cloud-native technologies.
• Automate the entire infrastructure life cycle (IaC) using Terraform and Terragrunt, ensuring secure, reproducible, and auditable environments.
• Implement and maintain CI/CD pipelines (e.g. GitHub Actions and TeamCity) and deployment tools like Flux and Helm for GitOps-driven application delivery.
• Optimize and manage the multi-tenant data layer on Postgres and Neon, focusing on robust tenant isolation, performance, backups, and safe schema management.

Operational excellence and reliability:

• Drive site reliability engineering (SRE) practices, including monitoring, alerting (Prometheus, Grafana), logging (Loki), and incident response.
• Solve complex operational challenges, such as optimizing scale to zero for cost efficiency, minimizing cold starts, enhancing autoscaling behavior, and managing queue backpressure.
• Implement platform-wide performance tuning (e.g. container resource limits, distributed locks, caching strategies, and GC configurations).
• Ensure platform security and compliance by implementing best practices for secrets management, network segmentation, and vulnerability scanning.

Technical leadership:

• Own major infrastructure roadmap items, including multi-region deployments, disaster recovery planning, advanced tenancy separation, and ephemeral preview environments.
• Champion DevOps and SRE principles across the engineering team, mentoring engineers on cloud-native best practices, operational readiness, and debugging complex distributed systems.
• Collaborate with product and engineering teams to define the long-term vision for the platform's architecture and operational model.

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

• Have five or more years of experience building and operating large-scale, commercial cloud-native infrastructure, with a strong focus on DevOps/SRE practices.
• Possess deep, hands-on expertise with GCP (or AWS/Azure) and Kubernetes administration and operations (GKE experience is a strong plus).
• Are proficient with infrastructure-as-code (IaC) tools, particularly Terraform, for managing complex environments.
• Have a solid understanding of Linux internals, networking (CNI and service mesh), security, and distributed system design.
• Are familiar with CI/CD tools, GitOps (e.g. Flux), monitoring stacks (Prometheus/Grafana), and logging systems.
• Thrive in cross-functional teams and excel at communicating complex infrastructure ideas clearly.

#LI-YY1

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’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.

As part of our team, you will:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• 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 product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 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 Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#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.

We just unveiled JetBrains Central,  a platform for agent-driven software development that connects tools, agents, and infrastructure. It enables automated AI workflows to run, be monitored, and be managed across teams – with clear visibility into results, costs, and performance.

JetBrains Central provides three core capabilities:
Governance and control: Policy enforcement, identity and access management, observability, auditability, and cost attribution for agent-driven work. Some of these functionalities are already available via the JetBrains Central Console.
Agent execution infrastructure: Cloud agent runtimes and computation provisioning that allow agents to run reliably across development environments.
Agent optimization and context: Shared semantic context across repositories and projects, enabling agents to access relevant knowledge, and task routing to the most appropriate models or tools.

Inside the Professional Services department, we are building a team of Forward Deployed Engineers to help our customers adopt AI-native development with JetBrains Central and enable JetBrains consulting partners to transform their customers’ AI-native software development lifecycle.

About the role

As a Principal Forward Deployed Engineer, you will work directly with early adopter customers to deploy JetBrains Central platform into complex enterprise environments and help them move from experimental AI usage to governed, scalable AI-native development.

You will act as a trusted advisor to customer engineering teams and consulting partners, while also mentoring other Forward Deployed Engineers and helping with complex deployments. In this role, you will help establish best practices for enterprise AI adoption and influence how organizations implement AI-driven software development.

This role is often a strong fit for engineers who have previously served as Principal or Staff Engineers, Heads of Engineering, CTOs, or Solutions Architects and who enjoy working directly with customers while staying deeply involved with complex technical systems.

This role combines consulting, architecture, and hands-on engineering. Your work in the field will directly influence how our platform evolves and how enterprises adopt AI-driven development.

You will work directly with the world’s most innovative companies — our client base includes 420 of the Fortune 500 — ensuring they maximize the value of their JetBrains investments.

What you’ll do

• Deploy JetBrains Central platform into enterprise development environments, CI/CD systems, and internal developer platforms.
• Help organizations transition from ad-hoc AI experimentation to governed AI-native development workflows.
• Implement enterprise AI governance, including security policies, access management, and data privacy controls.
• Enable observability for AI-driven development to help organizations understand their AI usage and its impact on productivity, as well as their code-generation patterns.
• Design sustainable AI adoption strategies, including usage controls and cost visibility across multiple model providers.
• Integrate our cloud-based agent execution layer, allowing AI agents to run securely and at scale.
• Collaborate closely with JetBrains product teams to shape the platform based on real-world deployments and customer feedback.
• Create delivery frameworks and train JetBrains partners to deliver the desired scope of service.
• Mentor and support other Forward Deployed Engineers, sharing architectural expertise and helping to scale the team’s deployment practices.

You will thrive in this role if you are

• A consultant at heart who can communicate effectively with both engineers and technical leadership.
• Able to see the whole picture of the software development lifecycle across developer tools, infrastructure, APIs, and cloud systems.
• Experienced at integrating complex software systems into enterprise environments.
• Curious about the future of AI agents and AI-native software development.
• Comfortable working with early adopters and overcoming ambiguous technical challenges.
• Motivated to mentor other engineers and contribute to building a new technical discipline.

What you will bring

• Experience in software-related projects, ideally in a customer-facing or consulting environment.
• Familiarity with Java or Python.
• Experience building or integrating developer platforms, infrastructure systems, or software solutions.
• Familiarity with modern development workflows (CI/CD, developer environments, internal developer platforms, etc.).
• Experience working directly with customers or technical stakeholders.
• Strong problem-solving skills and the ability to operate in fast-evolving technical environments.
• Ability and desire to travel to meet customers.

These would be a plus, but are not blockers

• Experience with industry-specific solutions (fintech, telco, retail, etc.)
• Experience with AI/LLM platforms, agent frameworks, or AI-assisted development tools.
  Experience in solutions engineering, consulting, or forward-deployed engineering roles.
• Familiarity with the JetBrains ecosystem or developer productivity tooling.

#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

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. Today, AI-powered coding agents are becoming a core part of how developers write Kotlin – and we want to make sure they write it well.

The Kotlin AI Value Stream team is responsible for how AI agents understand, generate, and improve Kotlin code across all platforms: Android, Kotlin Multiplatform, server-side, web, desktop, and others. We build the evaluation infrastructure, error analysis tools, and post-training pipelines that measure and improve agent behavior on real Kotlin developer tasks.

As a Research Engineer on this team, you'll own the end-to-end loop: Analyze how agents fail on Kotlin → build evals that capture those failures → research and implement methods to fix them → measure the improvement. Your work will directly shape how millions of developers experience Kotlin through AI coding agents.

As part of our team, you will:

Build tools for agentic error analysis

• Design and implement tooling to systematically capture, classify, and analyse errors that AI coding agents make when generating Kotlin code.
• Build observability pipelines over agentic traces – mining patterns from agent sessions in JetBrains IDEs, Junie, Claude Code, Cursor, and other coding agents.

Build evaluation pipelines

• Design, implement, and maintain evaluation pipelines that measure Kotlin code generation quality across dimensions, including correctness, idiomaticity, build success, framework usage, and test coverage.
• Build simulation environments where coding agents can be measured on realistic Kotlin developer tasks – from greenfield KMP projects and Gradle dependency management to migrating Spring applications from Java to Kotlin.
• Own evaluation infrastructure: metrics, experiment tracking, automated regression checks, and reproducible benchmarking.

Research methods for improving agent and model behavior on Kotlin

• Experiment with post-training techniques (SFT, DPO, GRPO) to improve how models handle Kotlin-specific patterns, idioms, and frameworks.
• Investigate context engineering approaches: CLAUDE.md/AGENTS.md files, compiler-as-verifier feedback loops, Kotlin LSP integration, and MCP-based tooling.
• Run experiments to measure impact: A/B comparisons, benchmark suites, and before/after analyses on real codebases.
• Collaborate with model providers (Anthropic, OpenAI, and Google) to translate Kotlin-specific findings into model improvements.

Build public Kotlin benchmarks

• Design and build open-source benchmarks that measure AI coding agent performance on Kotlin tasks and eventually become the standard reference for the ecosystem.
• Create task datasets covering the breadth of Kotlin usage: the server side (Spring, Ktor), multiplatform projects (KMP), build systems (Gradle), Android, library development, and others.
• Include both mined real-world tasks and carefully designed synthetic tasks that test specific Kotlin capabilities.
• Maintain and evolve benchmarks as models improve, ensuring they remain challenging, relevant, and contamination-resistant.

We'll be happy to have you on board if you have:

• Hands-on experience building evaluation or analysis pipelines for LLMs or AI coding agents in a research or production setting.
• Strong Python engineering skills (at least three years), with the ability to write clean, maintainable code in data-heavy and ML-adjacent codebases.
• Experience with data analysis at scale: querying large datasets (SQL/Athena), building data pipelines, and performing statistical analysis of experimental results.
• The ability to own projects end to end – from identifying a problem in agent traces to designing an eval, running experiments, and shipping a fix.
• A product-aware mindset: You care about how agents are actually used by developers and can translate real failure modes into evaluation and training work.
• Familiarity with Kotlin or a strong willingness to develop deep Kotlin expertise (you'll be living in Kotlin codebases daily).

Our ideal candidate would also have experience with:

• Post-training LLMs: SFT, RLHF, DPO, GRPO – either hands-on training or designing the data and reward pipelines that feed into training.
• Modern deep learning frameworks (PyTorch) and LLM training stacks (TRL, verl, Megatron, or similar).
• AI agent development: tool-using agents, multi-step coding workflows, agentic frameworks.
• Evaluation frameworks and tools: Inspect AI, Promptfoo, LM-evaluation-harness, or custom eval pipelines.
• Experiment tracking and observability: Weights & Biases, MLflow, Langfuse, or similar.
• The Kotlin ecosystem: Android, Gradle, KMP, Spring, Ktor – with an understanding of the developer workflows that agents need to support.
• Contributing to or maintaining open-source projects, especially benchmarks or evaluation tools.

Don't check every box? That's okay – if you're excited about this work and bring strong fundamentals, we'd love to hear from you. We're happy to talk and provide the training you need to grow into the role.

Why join JetBrains? 

• Strong base salary. We offer competitive pay that reflects your skills and experience.
• Flexible work location. Enjoy the freedom to work from home or from the office.
• Remote work. Spend up to 30 days per year working remotely from abroad.
• Extra time off. More days to relax, recharge, and do the things you love.
• Medical insurance allowance. Enjoy peace of mind for you and your family
• Learning and development opportunities. Access to conferences, courses, and language classes.
• Relocation support. We help make your move as smooth and stress-free as possible. 
• Language classes. Pick up the local language or sharpen your English skills.
• Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays.
• Mental health support. To help you feel your best, we provide easy access to professional mental health services.
• Sports benefit. Enjoy an on-site gym or sports club stipend.
• Internal events. Join company-wide celebrations and team gatherings.

*Some benefits may vary depending on location.

#LI-DNI