Job Overview

We are seeking a marketing analytics professional with a focus on data quality and AI-driven automation to improve marketing measurement, reporting, and decision-making. This role combines marketing analysis, data operations, and applied AI to ensure trusted, consistent data across multiple sources and to automate dashboards, attribution insights, and scalable analytical workflows.

Reports to

• Team Lead of Data & Marketing Technology

What will you do?

• Analyze marketing channels performance and deliver actionable recommendations
• Build and automate KPI reporting
• Support and automate experiment measurement (A/B, incrementality, creative testing)
• Design AI-powered automations for reporting, insight generation, anomaly detection, and campaign recommendations
• Own marketing/attribution data quality across sources (AppsFlyer + ad platforms), including validation and tracking troubleshooting
• Develop and maintain predictive models
• Partner with marketing, product, and engineering on tracking requirements, measurement plans, and documentation.
• Help maintain data pipelines/semantic layers; ensure metric definitions and transformation logic are clean and well-documented

What are we looking for?

• STEM education
• Strong foundation in marketing analytics: attribution, funnels, cohort/retention, LTV, ROAS/CAC, experimentation
• Hands-on experience with data analysis and automation (SQL + Python, BI tools, workflow automation)
• Familiarity with mobile attribution/measurement platforms (e.g., AppsFlyer) and multi-source marketing data integration
• Proactive mindset and eagerness to grow practical AI skills (ML fundamentals, LLM tooling, data quality automation)

What additional skills will help you stand out?

• Experience with warehouses (BigQuery/Snowflake), dbt, Airflow, or similar
• Experience with SKAdNetwork and privacy-safe attribution
• Experience operationalizing LLM workflows (prompting, evaluation, guardrails) for analytics use cases
• Experience with predictive modeling techniques (e.g., regression/classification, time-series forecasting, uplift/propensity modeling) and model evaluation

Work mode

• Hybrid (2-3 days of work from the office) 
• This role is eligible for relocation & immigration support.

Benefits

Benefits and perks are tailored to the local market and culture. Our benefits in Belgrade include:

• Additional vacation days based on years of service at Wargaming: up to 5 days on top of the statutory minimum
• Additional paid time off (5 Personal Days, Birthday Leave, Marriage Leave, Compassionate Leave)
• Sick Leave Compensation, Maternity Leave Benefits  
• Premium Private Health Insurance
• Career development and education opportunities within the company
• English clubs and platform for learning languages
• Mental well-being program (iFeel)
• Commuting allowance
• Company events
• FitPass membership
• Discounts for employees
• Personal Gaming Account
• Coffee, fruits, and snacks in the office
• On-site canteen with subsidized prices for food and drinks
• Seniority Awards
• Referral program - you can recommend the best talents to the Company and receive a reward

Please submit your CV in English to ensure smooth processing and review.

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’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