At a Glance
- Tasks: Design and conduct data collection tasks to ensure high-quality datasets for AI models.
- Company: Join a leading AI company focused on advancing technology and innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on cutting-edge research and development.
- Why this job: Make a real impact in AI by ensuring our models are robust and reliable.
- Qualifications: Strong software engineering skills and experience with data analysis and ML frameworks.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- Extremely strong software engineering skills
- Strong expertise in designing and conducting data collection tasks, including working with human annotators
- Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance
- Experience analysing datasets with respect to their quality, biases, and suitability for training ML models
- Hands-on experience training large language models (LLMs) on distributed training infrastructures
- Familiarity with evaluating and improving the generalisability and robustness of ML systems
- Proficiency in programming languages such as Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX)
- Excellent communication skills to collaborate effectively with cross-functional teams and present findings
- One or more papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP)
What the job involves
As a Member of Technical Staff in Data Analysis and Evaluation, you will play a pivotal role in ensuring the quality, reliability, and performance of our large language models (LLMs). Your primary focus will be on designing and conducting data collection tasks, assessing and evaluating dataset quality, and analysing the robustness and generalisability of our models. You will work closely with cross-functional teams, including researchers, engineers, and data annotators, to conduct data-driven decision-making and improve the overall effectiveness of our AI systems. This role combines expertise in statistics, experimental design, human annotators, and machine learning to ensure that our models are trained on high-quality data and perform reliably across diverse scenarios. You will contribute to Cohere’s mission of advancing AI by ensuring our systems are robust, scalable, and impactful.
Key Responsibilities
- Design and oversee data collection tasks, including supporting human annotators and ensuring data quality
- Develop and apply statistical methods to evaluate the quality and reliability of datasets
- Analyse and assess the generalisability and robustness of ML systems across diverse use cases
- Collaborate with teams to improve dataset quality and model performance
- Train and fine-tune large language models (LLMs) on distributed training infrastructures
- Conduct experiments to evaluate model performance and identify areas for improvement
Member of Technical Staff (Data Analysis and Evaluation) in London employer: Deepstreamtech
Cohere is an exceptional employer for those passionate about advancing AI technology, offering a collaborative work culture that fosters innovation and creativity. Located in a vibrant tech hub, employees benefit from continuous growth opportunities, access to cutting-edge resources, and the chance to work alongside industry leaders on impactful projects. With a strong emphasis on data quality and model performance, this role not only promises meaningful contributions but also supports professional development in a rapidly evolving field.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff (Data Analysis and Evaluation) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data analysis projects, especially those involving ML models. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in data analysis and ML. Practice common interview questions and be ready to discuss your past projects in detail.
✨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 (Data Analysis and Evaluation) in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your strong software engineering skills and expertise in data collection tasks. We want to see how you've used these skills in real-world scenarios, so don’t hold back on the details!
Talk About Your Experience:If you've got hands-on experience with large language models or have published papers at top-tier venues, let us know! This is your chance to shine and show us what you can bring to the table.
Be Clear and Concise:When writing your application, clarity is key. Use straightforward language to explain your experience and how it relates to the role. We appreciate a well-structured application that’s easy to read!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Deepstreamtech
✨Know Your Tech Inside Out
Make sure you brush up on your software engineering skills and be ready to discuss your experience with programming languages like Python and ML frameworks such as PyTorch or TensorFlow. Be prepared to share specific examples of how you've designed and conducted data collection tasks.
✨Show Off Your Statistical Savvy
Since the role requires strong statistical skills, come equipped with examples of how you've evaluated scientific experiments or assessed dataset quality. Think about how you can explain your approach to analysing biases and suitability for training ML models.
✨Collaboration is Key
This position involves working closely with cross-functional teams, so highlight your communication skills. Prepare to discuss how you've collaborated with researchers, engineers, and annotators in the past, and be ready to present findings clearly and effectively.
✨Bring Your Research to the Table
If you've published papers at top-tier venues, make sure to mention them! Discussing your research will not only showcase your expertise but also demonstrate your commitment to advancing AI. Be ready to talk about the impact of your work and how it relates to the role.
