At a Glance
- Tasks: Design and curate datasets to enhance AI model capabilities and run impactful experiments.
- Company: Join a pioneering tech firm focused on cutting-edge AI research.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on creativity and innovation.
- Why this job: Shape the future of AI by working with innovative datasets and technologies.
- Qualifications: 4+ years in machine learning and experience with multimodal datasets required.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- 4+ years of experience in machine learning, bonus points for data‑centric approaches
- Experience with large multimodal datasets and generative models (video, image, or multimodal)
- Deep intuition for how data composition and quality translate to model capabilities
- Comfort working across the full research stack: data analysis, dataset creation, model training, evaluation, and back again
- Proficiency with at least one ML framework (e.g. PyTorch, JAX) and distributed compute tools (e.g. Ray, Kubernetes)
- Excitement about building AI that simulates the world
What the job involves
- We are looking for a Research Engineer to own the data behind our models: what they learn from, how well they learn it, and what new capabilities that unlocks
- You will design datasets, run modeling experiments, and build the infrastructure to generate and curate data at scale — directly shaping what our models can do, with applications ranging from creative tools to robotics
- Design multimodal, multitask datasets that teach world models new capabilities — deciding what data to collect, generate, or curate and measuring its effect on model behavior
- Run controlled training experiments to understand how data composition drives model performance across tasks and domains
- Build and operate large‑scale pipelines for synthetic data generation, filtering, and quality control
- Define evaluations and benchmarks that measure whether our models are actually improving at the things that matter
- Partner with product and creative teams to translate target behaviors and capabilities into concrete data strategies
Member of Technical Staff (Research Engineer, Datasets) employer: Deepstreamtech
Join a forward-thinking company that prioritises innovation and collaboration, where as a Member of Technical Staff (Research Engineer, Datasets), you will play a pivotal role in shaping the future of AI. Our vibrant work culture fosters creativity and continuous learning, offering ample opportunities for professional growth while working with cutting-edge technologies in a supportive environment. Located in a dynamic tech hub, we provide unique advantages such as access to industry-leading resources and a network of like-minded professionals passionate about pushing the boundaries of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff (Research Engineer, Datasets)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and datasets. We want to see your work in action, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. We recommend practicing common ML scenarios and dataset challenges, as these will likely come up during your chats with hiring managers.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Member of Technical Staff (Research Engineer, Datasets)
Some tips for your application 🫡
Show Off Your Experience:Make sure to highlight your 4+ years of experience in machine learning. We want to see how you've tackled data-centric approaches and worked with large multimodal datasets. Don’t be shy about sharing specific projects or challenges you've faced!
Get Technical:We love it when candidates are proficient in ML frameworks like PyTorch or JAX. Be sure to mention any experience you have with distributed compute tools like Ray or Kubernetes. This is your chance to show us your technical chops!
Demonstrate Your Passion:Let your excitement for building AI that simulates the world shine through in your application. Share why this role excites you and how you envision contributing to our mission at StudySmarter. We’re looking for that spark!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at Deepstreamtech
✨Know Your Stuff
Make sure you brush up on your machine learning knowledge, especially around data-centric approaches. Be ready to discuss your experience with large multimodal datasets and generative models, as this will show your deep understanding of how data quality impacts model capabilities.
✨Showcase Your Projects
Prepare to talk about specific projects where you've designed datasets or run modelling experiments. Highlight the challenges you faced and how you overcame them, as well as any tools you used like PyTorch or Kubernetes. This will demonstrate your hands-on experience and problem-solving skills.
✨Ask Insightful Questions
Come prepared with questions that show your excitement about the role and the company’s mission. Ask about their current projects involving AI simulations or how they measure model performance. This not only shows your interest but also helps you gauge if the company is the right fit for you.
✨Be Ready to Collaborate
Since the role involves partnering with product and creative teams, be prepared to discuss how you’ve worked collaboratively in the past. Share examples of how you translated technical requirements into actionable data strategies, showcasing your ability to communicate effectively across disciplines.
