At a Glance
- Tasks: Develop cutting-edge ML models and optimize LLMs for diverse applications.
- Company: Join Scale, a rapidly growing team focused on international public sector projects.
- Benefits: Enjoy opportunities for travel, remote work flexibility, and collaboration with top-tier professionals.
- Why this job: Shape the future of AI while working on impactful projects in a dynamic environment.
- Qualifications: 2+ years in model training and deployment; strong NLP and deep learning skills required.
- Other info: Ideal candidates should be proficient in Arabic and have startup experience.
The predicted salary is between 43200 - 72000 £ per year.
Machine Learning Research Engineer, International Public Sector
Scale is growing rapidly, and joining the Global International Public Sector team is an opportunity to work on one of the most rapidly expanding teams at Scale. This team is responsible for generating, executing, and fostering Scale’s work outside of the United States. There are three core types of work involved:
- Building custom LLMs
- Providing high-quality training data for research institutions building LLMs from scratch
- Partnerships, upskilling, and advisory
As MLRE your focus will be on developing Models as a Service for our partners by optimizing LLMs through finetuning, RAG or other techniques. You will be involved end-to-end from coordinating with operations to create high quality datasets to productionizing models for our customers. If you are excited about shaping the future of the data-centric AI movement, we would love to hear from you!
You will:
- Study and implement cutting edge research in the field
- Design and implement agent workflows that leverage pre-training and fine tuning techniques to customize LLMs and embedding models for downstream tasks
- Understand customer needs
- Work with large unstructured data
- Build evaluation systems
- Work cross functionally with our data annotation teams and fine tune models on this data
- Travel up to 2 weeks per quarter to meet with the customer
Minimum Qualifications:
- At least 2+ years of model training, deployment and maintenance experience in a production environment
- Trained deep learning models + have built up that skillset
- Strong skills in NLP, LLMs and deep learning
- Ability and interest in traveling to the client site in the Middle East region at least one week each quarter
Ideal Qualifications:
- Proficient in reading and writing in Arabic
- Past experience working at a startup or in a forward-deployed role
- Has experience working cross functionally with operations
- Experience in dealing with large scale AI problems, ideally in the generative-AI field
- Demonstrated expertise in large vision-language models for diverse real-world applications, e.g. classification, detection, question-answering, etc.
- Published research in areas of machine learning at major conferences (NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals
- Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, Kuberflow, TensorFlow, etc.
- Strong written and verbal communication skills to operate in a cross functional team environment
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Machine Learning Engineer EPD United Kingdom employer: Scale AI, Inc.
Contact Detail:
Scale AI, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer EPD United Kingdom
✨Tip Number 1
Familiarize yourself with the latest advancements in NLP and LLMs. Being well-versed in cutting-edge research will not only help you during interviews but also demonstrate your passion for the field.
✨Tip Number 2
Showcase any experience you have with model training and deployment in a production environment. Be ready to discuss specific projects where you've optimized models or worked with large unstructured datasets.
✨Tip Number 3
Highlight your ability to work cross-functionally, especially if you have experience collaborating with data annotation teams. This is crucial for the role, so be prepared to share examples of successful teamwork.
✨Tip Number 4
If you have published research in machine learning, make sure to mention it. Discussing your contributions to major conferences can set you apart from other candidates and show your commitment to the field.
We think you need these skills to ace Machine Learning Engineer EPD United Kingdom
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description and understand the key responsibilities and qualifications required for the Machine Learning Research Engineer position. Tailor your application to highlight relevant experiences.
Highlight Relevant Experience: In your CV and cover letter, emphasize your experience with model training, deployment, and maintenance in a production environment. Mention specific projects where you have worked with NLP, LLMs, and deep learning.
Showcase Your Skills: Clearly outline your programming skills, especially in Python, and any frameworks or tools you are proficient in, such as DeepSpeed or TensorFlow. Provide examples of how you've used these skills in past projects.
Communicate Effectively: Since strong communication skills are essential for this role, ensure that your application is well-written and free of errors. Use clear and concise language to convey your ideas and experiences.
How to prepare for a job interview at Scale AI, Inc.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with model training, deployment, and maintenance. Highlight specific projects where you've worked with NLP, LLMs, and deep learning techniques, as these are crucial for the role.
✨Understand Customer Needs
Demonstrate your ability to understand and address customer requirements. Prepare examples of how you've previously tailored solutions to meet client expectations, especially in a cross-functional team setting.
✨Discuss Your Research Experience
If you have published research in machine learning, be ready to talk about it. Discuss the impact of your work and how it relates to the current trends in AI, particularly in generative AI and large vision-language models.
✨Prepare for Cross-Functional Collaboration
Since the role involves working with data annotation teams and operations, think of examples that showcase your collaborative skills. Be ready to explain how you’ve successfully worked across different teams to achieve project goals.