At a Glance
- Tasks: Develop and optimise synthetic data pipelines for advanced AI language models.
- Company: Join a leading AI company focused on innovative natural language processing solutions.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on research and engineering excellence.
- Why this job: Make a real impact in AI by transforming data into powerful language models.
- Qualifications: Strong Python skills and experience with data processing frameworks required.
The predicted salary is between 60000 - 80000 £ per year.
If you are passionate about transforming data into the foundation of AI systems, this role offers a unique opportunity to make a meaningful impact.
- 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 LLMs through work projects, open-source contributions or personal experimentation.
- Familiarity with LLM inference frameworks such as vLLM and TensorRT.
- Experience working with large-scale datasets, including web data, code data, and multilingual corpora.
- A passion for bridging research and engineering to solve complex data-related challenges in AI model training.
- (Desirable) Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).
What the job involves:
- As a Machine Learning Engineer specializing in synthetic data, you will play a pivotal role in developing the synthetic data pipeline that is crucial to Cohere’s advanced language models.
- Your responsibilities will encompass the end-to-end management of synthetic data, including maintaining and optimizing the synthetic data pipeline, data analysis and generation, as well as conducting data ablations and model evaluation to gauge data quality.
- You will work with diverse web data and code data and transform them using generative models to improve token efficiency and model quality.
- 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.
- Design and build scalable inference pipelines that run on large GPU clusters.
- Conduct data ablations to assess data quality and experiment with data mixtures to enhance model performance.
- Research and implement innovative synthetic 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.
Senior Member of Technical Staff (Synthetic Data) employer: Cohere
Cohere is an exceptional employer for those passionate about AI and data, offering a collaborative work culture that fosters innovation and creativity. Employees benefit from continuous growth opportunities through hands-on projects and cross-functional teamwork, all while working in a dynamic environment that values the intersection of research and engineering. Located in a vibrant tech hub, Cohere provides access to cutting-edge resources and a community of like-minded professionals dedicated to advancing natural language processing.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Member of Technical Staff (Synthetic Data)
✨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 projects, especially those involving Python and data pipelines. This is your chance to demonstrate your expertise in handling large-scale datasets and working with LLMs.
✨Tip Number 3
Prepare for interviews by brushing up on technical questions related to synthetic data and AI models. Practice explaining your past projects and how they relate to the role, focusing on your problem-solving skills and engineering mindset.
✨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 mission at Cohere.
We think you need these skills to ace Senior Member of Technical Staff (Synthetic Data)
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your strong software engineering skills, especially in Python. We want to see your experience with data pipelines and any frameworks like Apache Spark or Pandas that you've worked with.
Talk About Your Experience:If you've worked with large-scale datasets or have experience with LLMs, let us know! Share specific projects or contributions that showcase your ability to bridge research and engineering.
Be Passionate:We love candidates who are genuinely passionate about AI and data. In your application, express your enthusiasm for transforming data into something impactful and how you can contribute to our mission.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensure it gets the attention it deserves.
How to prepare for a job interview at Cohere
✨Know Your Tech Inside Out
Make sure you brush up on your Python skills and get familiar with data processing frameworks like Apache Spark and Pandas. Be ready to discuss your experience with building data pipelines and how you've tackled challenges in previous projects.
✨Showcase Your Passion for AI
This role is all about transforming data for AI systems, so be prepared to share your enthusiasm for the field. Talk about any personal projects or open-source contributions related to LLMs that demonstrate your commitment and creativity.
✨Prepare for Technical Questions
Expect some deep dives into your technical knowledge, especially around synthetic data and model evaluation. Brush up on concepts related to data ablations and how they impact model performance, as well as any relevant research you've done.
✨Collaborate and Communicate
Since you'll be working with cross-functional teams, practice articulating your ideas clearly. Think of examples where you've successfully collaborated with others, and be ready to discuss how you can bridge the gap between research and engineering.
