Sr Data Scientist Machine Learning Engineer - GenAI LLM

Databricks
London
3 weeks ago
Create job alert

CSQ326R77

Mission

The Machine Learning (ML) Practice team is a highly specialized customerfacing 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 crossfunctionally to shape longterm 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 DataAI Summit
  • Provide technical mentorship to the larger ML SME community in Databricks
  • Collaborate crossfunctionally 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 finetuning and deploying LLMs with tools such as HuggingFace Langchain and OpenAI
  • Extensive handson industry data science experience leveraging typical machine learning and data science tools including pandas scikitlearn and TensorFlow/PyTorch
  • Experience building productiongrade machine learning deployments on AWS Azure or GCP
  • Graduate degree in a quantitative discipline (Computer Science Engineering Statistics Operations Research etc. or equivalent practical experience
  • Experience communicating and/or teaching technical concepts to nontechnical and technical audiences alike
  • Passion for collaboration lifelong learning and driving business value through ML
  • Preferred Experience working with Databricks & Apache Spark to process largescale distributed datasets


Required Experience:

Senior IC


Key Skills
Apache Hive,S3,Hadoop,Redshift,Spark,AWS,Apache Pig,NoSQL,Big Data,Data Warehouse,Kafka,Scala
Employment Type :Full Time
Experience:years
Vacancy:1

Related Jobs

View all jobs

Senior Data Scientist

Sr Data Science Manager, Professional Services ...

Sr. Data Engineer, GOX - Global Operational Excellence

Sr Business Development Manager Advertising Measurement and Data Science MADS Amazon Advertising

Director, Data Engineering Solutions - Growth

Senior Machine Learning Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.

Quantum-Enhanced Machine Learning—Propelling AI into the Next Frontier

Machine learning (ML) has revolutionised how we interpret data, build predictive models, and create intelligent applications. From recommendation engines and self-driving cars to advanced genomics and natural language processing, ML solutions are integral to nearly every corner of modern life. However, as data complexity and model size continue to skyrocket, the computational demands placed on ML systems grow in tandem—often pushing even high-performance classical computers to their limits. In recent years, quantum computing has emerged as a tantalising solution to these challenges. Unlike traditional digital systems, quantum computers exploit quantum mechanics—superposition and entanglement—to process information in ways that defy conventional logic. As these machines mature, they promise exponential speed-ups for certain tasks, potentially reshaping how we approach AI and data-intensive challenges. What does this mean for machine learning? Enter quantum-enhanced ML, a new frontier where quantum processors and classical ML frameworks unite to accelerate model training, tackle high-dimensional data, and solve complex optimisation tasks more efficiently. In this article, we will: Unpack the current state of machine learning, highlighting key bottlenecks. Provide a concise overview of quantum computing—why it’s radical and how it differs from classical technology. Examine potential breakthroughs in quantum-enhanced ML, including real-world use cases and technical approaches. Explore the roles and skill sets that will define this quantum-AI era, with guidance on how to prepare. Discuss the roadblocks (like hardware maturity and ethical concerns) and how they might be addressed in the years to come. If you’re a machine learning engineer, data scientist, or simply an AI enthusiast fascinated by the next wave of computational innovation, read on—quantum computing could become an integral part of your future toolkit, opening up job opportunities and reimagining what ML can achieve.

Machine Learning Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Machine learning (ML) has become the beating heart of modern tech innovation, powering breakthroughs in healthcare, finance, cybersecurity, robotics, and more. Across the United Kingdom, this surge in ML-driven solutions is fueling the success of countless start-ups—and spurring demand for talented machine learning engineers, data scientists, and related professionals. If you’re eager to join a high-growth ML company or simply want to keep tabs on the latest trends, this Q3 2025 Investment Tracker will guide you through the newly funded UK start-ups pushing the boundaries of ML. In this article, we’ll highlight key developments from Q3 2025, delve into the most promising newly funded ventures, and shed light on the machine learning roles they’re urgently seeking to fill. Plus, we’ll show you how to connect with these employers via MachineLearningJobs.co.uk, a dedicated platform for ML job seekers. Let’s dive in!