At a Glance
- Tasks: Design and curate datasets, run experiments, and build data pipelines for AI models.
- Company: Join a cutting-edge AI company focused on innovative research.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement potential.
- Why this job: Shape the future of AI by working with multimodal datasets and advanced technologies.
- Qualifications: 4+ years in machine learning, experience with ML frameworks, and strong data intuition.
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) in London employer: Deepstreamtech
Join a forward-thinking company that prioritises innovation and collaboration, where as a Member of Technical Staff (Research Engineer, Datasets), you will have the opportunity to work with cutting-edge technology in a dynamic environment. Our culture fosters continuous learning and growth, offering employees access to professional development resources and a supportive team atmosphere. Located in a vibrant tech hub, we provide unique advantages such as networking opportunities and a diverse community of like-minded professionals passionate about shaping the future of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff (Research Engineer, Datasets) in London
✨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 interview questions and coding challenges to boost your confidence and impress your interviewers.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. 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) in London
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 comfortable with the full research stack. Be sure to mention your proficiency with ML frameworks like PyTorch or JAX, and any experience with distributed compute tools like Ray or Kubernetes. This will show us you’re ready to dive right in!
Data is Key:Since this role is all about owning the data behind our models, talk about your experience in designing datasets and running experiments. Share examples of how you've measured the impact of data composition on model performance. We want to know how you think about data!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, we can’t wait to see what you bring to the table!
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 that you understand the core of what the role entails.
✨Showcase Your Projects
Prepare to talk about specific projects where you've designed datasets or run experiments. Highlight how your work has directly influenced model performance and capabilities. This will demonstrate your hands-on experience and your ability to connect data quality with model outcomes.
✨Familiarise with Tools
Get comfortable with the ML frameworks mentioned in the job description, like PyTorch or JAX, and any distributed compute tools such as Ray or Kubernetes. Being able to discuss your proficiency with these tools will give you an edge and show that you're ready to hit the ground running.
✨Think Like a Researcher
Be prepared to discuss your approach to designing experiments and evaluating model performance. Think about how you would measure improvements and what benchmarks you would set. This will highlight your research mindset and your ability to contribute to the team’s goals.
