At a Glance
- Tasks: Design and build scalable data pipelines for diverse datasets in AI.
- Company: Cohere, a leader in AI research and development.
- Benefits: Inclusive culture, weekly lunch stipend, and collaboration with top experts.
- Other info: Join a diverse team passionate about shaping the future of AI.
- Why this job: Transform data into the foundation of cutting-edge AI systems and make a real impact.
- Qualifications: Strong Python skills and experience with data processing frameworks.
The predicted salary is between 36000 - 60000 £ per year.
Who are we? Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI.
Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products. Join us on our mission and shape the future!
Why this role? As a Machine Learning Engineer specializing in pretraining data, you will play a pivotal role in developing the data pipeline that underpins Cohere’s advanced language models. Your responsibilities will encompass the end-to-end management of training data, including ingestion, cleaning, filtering, and optimization, as well as data modeling to ensure datasets are structured and formatted for optimal model performance. You will work with diverse data sources—such as web data, code data, multilingual corpora, and synthetic data—to ensure their quality, diversity, and reliability. In this role, you will design and implement scalable, robust pipelines for data processing, conduct data ablations to evaluate quality, and experiment with data mixtures to enhance model performance. By combining research and engineering, you will bridge the gap between raw data and cutting-edge AI models, directly contributing to improvements in critical training metrics like throughput and accelerator utilization. Your work will be essential to Cohere’s mission of delivering efficient and reliable language understanding and generation capabilities, driving innovation in natural language processing.
If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a meaningful impact.
As a Machine Learning Engineer (Pre-Training Data), You Will:
- Design and build scalable data pipelines to ingest, clean, filter, and optimize diverse datasets, including web data, code data, multilingual corpora, and synthetic data.
- Conduct data ablations to assess data quality and experiment with data mixtures to enhance model performance.
- Develop robust data modeling techniques to ensure datasets are structured and formatted for optimal training efficiency.
- Research and implement innovative data curation methods, leveraging Cohere’s infrastructure to drive advancements in natural language processing.
- Collaborate with cross-functional teams, including researchers and engineers, to ensure data pipelines meet the demands of cutting-edge language models.
You May Be a Good Fit If You Have:
- Strong software engineering skills, with proficiency in Python and experience building data pipelines.
- Familiarity with data processing frameworks such as Apache Spark, Apache Beam, Pandas, or similar tools.
- Experience working with large-scale datasets, including web data, code data, and multilingual corpora.
- Knowledge of data quality assessment techniques and experimentation with data mixtures.
- A passion for bridging research and engineering to solve complex data-related challenges in AI model training.
Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).
If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! If you want to work really hard on a glorious mission with teammates that want the same thing, Cohere is the place for you. We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.
Full-Time Employees At Cohere Enjoy These Perks:
- An open and inclusive culture and work environment.
- Work closely with a team on the cutting edge of AI research.
- Weekly lunch stipend, in-office lunches.
Member of Technical Staff, Pre-Training Data employer: Cohere
Cohere is an exceptional employer that champions innovation and collaboration, particularly in the dynamic field of AI deployment. With a flexible remote work environment, generous vacation policies, and robust training stipends, we prioritise employee well-being and professional growth. Join us to be part of a forward-thinking team that is making significant impacts in sectors like finance and healthcare.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff, Pre-Training Data
✨Tip Number 1
Network like a pro! Reach out to current employees at Cohere on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing a role like the Member of Technical Staff. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for technical interviews by brushing up on your Python skills and data pipeline knowledge. Practice coding challenges and be ready to discuss your past projects. We want to see how you think and solve problems, so show us your best work!
✨Tip Number 3
Don’t just apply—engage! When you submit your application through our website, follow up with a friendly email expressing your enthusiasm for the role. Let us know why you’re excited about working with cutting-edge AI models and how you can contribute.
✨Tip Number 4
Showcase your passion for AI and data! In interviews, share your thoughts on the future of natural language processing and how you see yourself contributing to our mission. We love candidates who are genuinely excited about what we do!
We think you need these skills to ace Member of Technical Staff, Pre-Training Data
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the role of a Machine Learning Engineer. Highlight your software engineering skills, especially in Python, and any relevant experience with data pipelines.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about transforming data into AI systems. Share specific examples of your work with large-scale datasets and how you've tackled data-related challenges.
Showcase Your Projects:If you've worked on projects involving data processing frameworks or have published papers, make sure to include them. This gives us insight into your hands-on experience and your ability to bridge research and engineering.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to shape the future of AI!
How to prepare for a job interview at Cohere
✨Know Your Data Inside Out
Make sure you’re well-versed in the types of datasets mentioned in the job description, like web data and multilingual corpora. Be ready to discuss your experience with data ingestion, cleaning, and filtering, as well as any specific projects where you’ve optimised data for model performance.
✨Showcase Your Engineering Skills
Brush up on your Python skills and be prepared to talk about your experience with data processing frameworks like Apache Spark or Pandas. You might even want to bring examples of data pipelines you've built or optimised in the past to demonstrate your technical prowess.
✨Prepare for Technical Questions
Expect questions that dive deep into data quality assessment techniques and experimentation with data mixtures. Think about how you would approach a problem involving data ablation or how you would ensure the reliability of diverse datasets. Practising these scenarios can really help you shine.
✨Emphasise Collaboration
Since this role involves working with cross-functional teams, be ready to share examples of how you’ve collaborated with researchers and engineers in the past. Highlight your ability to bridge the gap between research and engineering, as this is crucial for the success of the role.
