At a Glance
- Tasks: Develop and train Large Language Models for coding tasks in a collaborative environment.
- Company: Join JetBrains, a leader in developer tools since 2000, passionate about innovation.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Dynamic team with access to advanced GPU clusters and a commitment to inclusivity.
- Why this job: Make a real impact by working on cutting-edge AI technology that empowers developers worldwide.
- Qualifications: Experience in ML systems, strong NLP background, and proficiency in deep learning frameworks.
The predicted salary is between 63000 - 77000 £ per year.
At Jet Brains, 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 Jet Brains 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 Py Torch 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 v LLM, Deep Speed, Tensor RT.
- 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 Jet Brains AI
- A cluster of hundreds of NVIDIA GPUs as training infrastructure.
- Git for source control management.
- Python, Py Torch, and Hugging Face as an ML stack.
- Kubeflow and Weights & Biases for experiment tracking.
- Team City as a CI Automation system.
- #LI-KP1
- We are an equal opportunity employer
We know great ideas can come from anyone, anywhere.
That's why we do our best to create an open and inclusive workplace - one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation.
We process the data provided in your job application in accordance with the Recruitment Privacy Policy.
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Staff Research Engineer (LLM Pre-Training) employer: JetBrains
JetBrains is an exceptional employer that fosters a dynamic and innovative work culture, where your expertise in Intellectual Property and Data Protection Law will be valued and impactful. Located in the United Kingdom, we offer competitive benefits, a commitment to employee growth through continuous learning opportunities, and a collaborative environment that encourages creativity and professional development. Join us to be part of a forward-thinking team that is shaping the future of technology.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Research Engineer (LLM Pre-Training)
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We think you need these skills to ace Staff Research Engineer (LLM Pre-Training)
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at JetBrains. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at JetBrains
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at JetBrains!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
