At a Glance
- Tasks: Design and implement strategies for LLM training datasets and collaborate on innovative ML projects.
- Company: Fast-growing deep-tech company leading in quantum software and AI.
- Benefits: Signing bonus, relocation package, flexible hours, and equal pay.
- Why this job: Join a multicultural team at the forefront of AI and quantum computing.
- Qualifications: Master’s/Ph.D. in relevant fields and 4+ years in data science or machine learning.
- Other info: Opportunity to mentor others and contribute to cutting-edge research.
The predicted salary is between 48000 - 84000 £ per year.
Overview
At RemoteStar, we\’re currently hiring for one of our client based in Spain.
9-month fixed-term contract | Hybrid (3 days/week onsite) | Location: Barcelona or Madrid
About client
Well-funded and fast-growing deep-tech company founded in 2019. We are the biggest Quantum Software company in the EU. They are also one of the 100 most promising companies in AI in the world (according to CB Insights, 2023) with 150+ employees and growing, fully multicultural and international.
They provide hyper-efficient software to companies seeking to gain an edge with quantum computing and artificial intelligence. Their main products, Singularity and CompactifAI, address critical needs across various industries.
Required Qualifications
- Master’s, or Ph.D. in Computer Science, AI, Data Science, Physics, Math, or a related field. Or equivalent industry experience.
- 4+ years of experience in data science, machine learning, or related roles, with demonstrated experience with NLP or LLMs.
- In-depth knowledge of large foundational model architectures (language and multimodal models) and their lifecycle: training, fine-tuning, alignment, and evaluation.
- Proficient in Python and data tooling ecosystems (Pandas, NumPy, Hugging Face Datasets & Transformers libraries).
- Hands-on experience with text data collection from diverse sources: web scraping, APIs, proprietary corpora, etc.
- Strong understanding of data quality metrics including bias detection, toxicity, and readability.
- Experience working in large shared distributed computing environments, familiarity with relevant tools for hardware optimization (vLLM, TensorRT, NeMo, etc.).
- Experience with version control (git), unit testing, and other fundamental aspects of software development.
- Effective communication and interpersonal abilities.
Preferred Qualifications
- Experience building or contributing to datasets used in LLM pretraining or supervised fine-tuning.
- Experience building foundational LLMs from the ground up
- Familiarity with alignment techniques (e.g., reinforcement learning, preference modeling, reward modeling).
- Exposure to multilingual and low-resource language datasets.
- Contributions to open-source datasets, tools, or publications in dataset-centric research.
- Knowledge of ethical AI, data governance, privacy laws (e.g., GDPR), and responsible data use.
- Familiarity with the software development lifecycle and agile methodologies
As a Senior LLM Engineer, you will
- Design and implement strategies for creating, sourcing, and augmenting datasets tailored for LLM training and fine-tuning.
- Develop scalable pipelines to collect, clean, filter, annotate, and validate large volumes of text data, ensuring quality, ethical compliance, etc.
- Collaborate with ML engineers, researchers, and software engineers to achieve ambitious goals in the preparation of LLMs and complementary work (preparing datasets, model evaluation, model serving, etc.).
- Develop and integrate new routines for modifying and enhancing LLMs, and extending their functionality.
- Make effective use of distributed compute resources and clusters (GPU’s), identify opportunities for further optimization.
- End-to-end preparation of compressed and specialized LLMs for use in production.
- Keep up to date with research trends in LLM foundation models, dataset curation, LLM pretraining data, and benchmarking.
- Contribute to building documentation, development standards, and a healthy shared code base.
- Mentor other engineers and provide knowledge sharing of cutting-edge techniques.
We offer
- Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion.
- Relocation package (if applicable).
- Up to 9-month contract, ending on June 2026.
- Hybrid role and flexible working hours.
- Be part of a fast-scaling Series B company at the forefront of deep tech.
- Equal pay guaranteed.
- International exposure in a multicultural, cutting-edge environment.
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Senior Machine Learning Engineer (Spain) employer: RemoteStar
Contact Detail:
RemoteStar Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer (Spain)
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or conferences related to machine learning and AI. You never know who might have a lead on your dream job or can introduce you to someone at RemoteStar.
✨Show Off Your Skills
Create a portfolio showcasing your projects, especially those involving NLP or LLMs. Share your work on platforms like GitHub or even your own website. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Ace the Interview
Prepare for technical interviews by brushing up on your Python skills and understanding large foundational model architectures. Practice common interview questions and be ready to discuss your past experiences in detail. Confidence is key!
✨Apply Through Us!
Don’t forget to apply through our website! We’re here to help you land that Senior Machine Learning Engineer role at RemoteStar. Plus, we’ve got loads of resources to support you throughout the process.
We think you need these skills to ace Senior Machine Learning Engineer (Spain)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Senior Machine Learning Engineer. Highlight your experience with NLP, LLMs, and any relevant projects that showcase your skills in Python and data tooling. We want to see how your background aligns with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about quantum computing and AI. Share specific examples of your work that relate to the job description, and let us know why you want to join our client’s team in Spain.
Showcase Your Projects: If you've worked on any interesting projects, especially those involving large foundational models or dataset curation, make sure to mention them. We love seeing practical applications of your skills, so don’t hold back on sharing your achievements!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at RemoteStar
✨Know Your Stuff
Make sure you brush up on your knowledge of large foundational model architectures and their lifecycle. Be ready to discuss your hands-on experience with NLP, LLMs, and the tools mentioned in the job description, like Python, Pandas, and Hugging Face. This will show that you're not just familiar with the theory but have practical skills too.
✨Showcase Your Projects
Prepare to talk about specific projects where you've built or contributed to datasets for LLM training. Highlight any experience with ethical AI and data governance, as these are crucial in today's tech landscape. Bring examples of your work that demonstrate your ability to handle text data collection and quality metrics.
✨Collaboration is Key
Since you'll be working closely with ML engineers and researchers, emphasise your teamwork skills. Share examples of how you've collaborated on projects in the past, especially in distributed computing environments. This will help them see you as a team player who can contribute to ambitious goals.
✨Stay Current
Keep yourself updated on the latest research trends in LLM foundation models and dataset curation. Mention any recent papers or developments you've followed, and be prepared to discuss how they might influence your work. This shows your passion for the field and your commitment to continuous learning.