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

The Firm

XTX Markets is a leading algorithmic trading firm which uses state-of-the-art machine learning technology to produce price forecasts for over 50,000 financial instruments across equities, fixed income, currencies, commodities and crypto. It uses those forecasts to trade on exchanges and alternative trading venues, and to offer differentiated liquidity directly to clients worldwide. The firm trades over $250bn a day across 35 countries and has over 250 employees based in London, New York, Singapore, Paris, Bristol, Mumbai, Yerevan and Kajaani.

We leverage the talent of the people who work here, modern computational techniques and state-of-the-art research infrastructure to analyse large data sets across markets quickly and efficiently, to maximize the effectiveness of our proprietary trading algorithms. We are actively seeking new methods and ideas. The models that drive our trading strategies have evolved considerably over the last 10 years, from econometric methods that gave our company its name, to trees, to neural networks, to modern deep learning architectures.

XTX Markets has an unrivalled level of computational resources in the trading industry, with a growing research cluster currently containing over 25,000 GPUs with 650 petabytes of usable storage. Teams across the firm include world-class researchers, developers and technologists with backgrounds in pure math, programming, physics, computer science and machine learning. The firm is also constructing a large-scale data centre in Finland to future-proof its significant computational capabilities.

At XTX Markets technology is our business and we are a diverse organization which attracts outstanding talent from across all industry backgrounds. We are focused on teamwork and our people collaborate on all aspects of the business, working openly and with respect for each other, our clients and the market. Our culture is non-hierarchical and one where everyone is valued. We strive for excellence in everything we do.

The Role

We are seeking a Software Engineer for our office in Yerevan, Armenia, who can help to automate the work done by our Quantitative Researchers. Having robust, scalable code which underpins the performance and reliability of the components is vital to the success of the firm’s research. You will partner closely with Quantitative Researchers to develop a wide range of software. 

Within your time with us you might:
 
•    Automate workflows for researchers; streamlining, productionising, and testing new models implemented by the Quantitative Researchers (mostly Python). 
•    Organise and support production code, checking dependencies, handling missing data, raising timely alerts, and ensuring informative logging etc (mostly Python). 
•    Develop ETL processes to handle a vast range of data.
•    Write unit and integration tests (googletest, pytest, unittest.mock).
•    Contribute to research libraries (mostly C++).
•    Code review (C++ and Python).

We operate with great agility, and the impact of your work will be both visible and large. Please note, however, that the role does not involve implementation of quant algorithms.

Essential Attributes

• At least 3 years of experience as a Software Engineer in a leading firm with Python as a main language.
• A history of high academic achievement, with at least an undergraduate degree (upper second class or better, or international equivalent) from a well-regarded educational establishment in Computer Science or a related technical field.
• Competency in C++.
• Ability to write Python libraries that would be used by quants.
• Understanding of the operational, maintenance, monitoring and support aspects of a business-critical system.
• Ability to proactively identify problems, bottlenecks, ways to improve workflow and software.
• Implementing software workflows / testing and deployment methodologies in an extremely agile environment.
• At least intermediate level of both English and Russian.
  
  
  

Desirable Experience

• Workflow automation systems (airflow, prefect, dagster).
• ETL, ELT processes (pandas, dask, spark, parquet).
• Time-Series DataBases (TimescaleDB, VictoriaMetrics, Prometheus, InfluxDB)
• Systems monitoring tools (Dash, Grafana, Sentry, ELK).
• Scientific python instruments (numpy, pandas, scipy, cython).
• Computational Mathematics.
• Data describing libraries (dataclasses, pydantic)
• Static code analysis tools (ruff, pylint, mypy, pyright, flake8).
• Cloud services (AWS, GCS)
• Performance (CPU, Memory, Filesystem) profiling and optimization.
• CI/CD technologies (Github Actions, Gitlab CI, Jenkins).

Benefits

• Working in a collaborative and friendly culture in a diverse team of outstanding talented people from across all industry backgrounds.
• Wellness allowance.
• Extensive medical benefits for employees and their dependents.
• Personal accident and critical illness insurance.
• Catered meals in the office.
• Weekly corporate massage sessions in the office.
• Relocation support for candidates residing outside of Yerevan, Armenia.
• 25 days holiday per year + statutory holiday and paid sick days.
• We work 4 days per week in the office, 1 (at one's discretion) from home unless stated otherwise.
  
  

Hiring Process

• 4 interviews (phone/video/onsite).
• Coding exercise.
• Professional references check.
• Background check.