At a Glance
- Tasks: Develop and optimise LLM solutions while collaborating with cross-functional teams.
- Company: Join Databricks, a leading data and AI company trusted by over 10,000 organisations globally.
- Benefits: Enjoy comprehensive benefits, remote work options, and a commitment to diversity and inclusion.
- Why this job: Fuel your curiosity in ML trends and make a real impact in a collaborative environment.
- Qualifications: Experience in Generative AI applications and strong data science skills are essential.
- Other info: Opportunity to present at conferences and mentor within the ML community.
The predicted salary is between 43200 - 72000 £ per year.
The Machine Learning (ML) Practice team is a highly specialized customer-facing ML team at Databricks facing an increasing demand for Large Language Model (LLM)-based solutions. We deliver professional services engagements to help our customers build, scale, and optimize ML pipelines, as well as put those pipelines into production. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in LLMs, MLOps, and ML more broadly.
The impact you will have:
- Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
- Build, scale, and optimize customer data science workloads and apply best in class MLOps to productionize these workloads across a variety of domains
- Advise data teams on various data science such as architecture, tooling, and best practices
- Present at conferences such as Data+AI Summit
- Provide technical mentorship to the larger ML SME community in Databricks
- Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap
What we look for:
- Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI
- Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch
- Experience building production-grade machine learning deployments on AWS, Azure, or GCP
- Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike
- Passion for collaboration, life-long learning, and driving business value through ML
- [Preferred] Experience working with Databricks & Apache Spark to process large-scale distributed datasets
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow. To learn more, follow Databricks on Twitter ,LinkedIn and Facebook .
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit .
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
#J-18808-Ljbffr
Sr. Data Scientist / Machine Learning Engineer - GenAI (London) employer: Databricks
Contact Detail:
Databricks Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Sr. Data Scientist / Machine Learning Engineer - GenAI (London)
✨Tip Number 1
Familiarise yourself with the latest trends in Generative AI and Large Language Models. Being able to discuss recent advancements or case studies during your interview can demonstrate your passion and knowledge in the field.
✨Tip Number 2
Engage with the Databricks community on platforms like LinkedIn or Twitter. This can help you understand their culture better and may even lead to connections that could provide insights or referrals for your application.
✨Tip Number 3
Prepare to showcase your experience with MLOps and production-grade machine learning deployments. Be ready to discuss specific projects where you've successfully implemented these practices, as this is a key focus for the role.
✨Tip Number 4
Consider brushing up on your presentation skills. Since the role involves presenting at conferences, being able to communicate complex technical concepts clearly will be crucial in making a strong impression.
We think you need these skills to ace Sr. Data Scientist / Machine Learning Engineer - GenAI (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in building Generative AI applications and working with tools like HuggingFace, Langchain, and OpenAI. Emphasise your hands-on industry data science experience and any production-grade machine learning deployments you've worked on.
Craft a Compelling Cover Letter: In your cover letter, express your passion for collaboration and lifelong learning. Mention specific projects or experiences that demonstrate your ability to communicate technical concepts to both technical and non-technical audiences.
Showcase Your Technical Skills: Include a section in your application that lists your technical skills, particularly those related to ML tools such as pandas, scikit-learn, TensorFlow/PyTorch, and cloud platforms like AWS, Azure, or GCP. This will help the hiring team quickly see your qualifications.
Highlight Relevant Projects: If you have experience presenting at conferences or mentoring others in the ML community, be sure to include this in your application. It shows your commitment to the field and your ability to contribute to the Databricks team.
How to prepare for a job interview at Databricks
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with Generative AI applications and the tools mentioned in the job description, such as HuggingFace and Langchain. Highlight specific projects where you've built or deployed LLMs, as this will demonstrate your hands-on experience.
✨Communicate Clearly
Since the role involves advising both technical and non-technical teams, practice explaining complex concepts in simple terms. This will show your ability to bridge the gap between different audiences, which is crucial for collaboration.
✨Demonstrate Your Passion for Learning
Express your enthusiasm for staying updated on the latest trends in ML and LLMs. Share examples of how you've pursued continuous learning, whether through courses, conferences, or personal projects, to show that you're committed to growth.
✨Prepare for Cross-Functional Collaboration
Think of examples where you've successfully worked with cross-functional teams. Be ready to discuss how you influenced product roadmaps or collaborated with engineering teams, as this aligns with the collaborative nature of the role.