Delivering the best Spotify experience possible, to as many people as possible, in as many moments as possible. That's what the Experience team is all about. We use our deep understanding of consumer expectations to enrich the lives of millions of our users all over the world, bringing the music and audio they love to the devices, apps and platforms they use every day. Know what our users want? Join us and help Spotify give it to them.
As a Staff Machine Learning Engineer in our Content Understanding teams, you will help define and build ML deployed at scale in support of a broad range of use cases driving value in media and catalog understanding. We are looking for a strong Staff Level MLE with experience in MLOps supporting Machine Learning systems at scale in production. This role will be for a team focused on Text/NLP/LLMs. Here are some examples of the work you may support:
\n What You'll DoYou will be the technical lead for a small team of engineers building and maintaining text understanding tools for transcription, moderation, entity extraction and resolution, topic modeling, and summarization (to name a few)You will be responsible for crafting and delivering solutions involving Large Language Models, including training, fine-tuning, and maintaining these modelsYou will team up with our Product and Engineering leadership to shape the future vision for text understanding tooling.You will conduct thorough research and analysis of the latest industry trends in LLM developments and introduce groundbreaking use cases for internal use cases.Collaborate with technical and non-technical business partners to develop analytics and metrics that describe the performance of our text understanding systems and the quality of our data.Be a leading voice in an active community of machine learning practitioners across Spotify and leverage existing innovative tooling in the Spotify ecosystem. (TensorFlow, Kubeflow, DataFlow, python-beam, Google Cloud Platform).Contribute to our team-wide product ideation in collaboration with other specialists, researchers, product managers, and subject-matter experts on the team. Who You AreBoth academic and proven experience in text understanding and natural language processing solutions, such as Large Language Models (LLMs) and Speech-to-Text transcriptionExpertise with training, fine-tuning, deploying, and maintaining LLMs, including experience working with popular third-party foundation models, such as Llama, OpenAI, and GeminiFamiliarity with various fine-tuning techniques in the LLM domain, such as RAG and LoRA adaptersExtensive experience working in a product and data-driven environment (Python, Scala, Java, SQL, or C++, with Python experience required) and cloud platforms (GCP or AWS).Understand storage solutions and when to use them (e.g. Graph Database, Cassandra, Relational database).You have experience architecting data pipelines and are self-sufficient in getting the data you need to build and evaluate models, using tools like Dataflow, Apache Beam, or Spark.You have a good understanding of MLOps and the model lifecycle (AB/Testing, experimentation, push and maintaining models in production, observability, and monitoring)You're familiar with the industry trends and keep up with the latest product offerings, and can understand trade-offs of existing solutions.You have excellent communication skills and the ability to translate business intuition into data-driven hypotheses that result in impactful engineering solutions.You love your customers even more than your code.You have experience and passion for mentoring and encouraging collaborative teams.You have experience in cultivating a strong engineering culture in an agile environment. Where You'll BeWe are a distributed workforce enabling our band members to find a work mode that is best for them!Where in the world? For this role, it can be within the Eastern US region or located in London, UK in which we have a work location and is within working hours. Working hours? We operate within the Eastern US time zones for collaboration and ask that all be able to work within that time zone. Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.
\nThe United States base range for this position is $205,800 - $294,000, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(K) retirement plan, 23 paid days off, 13 paid flexible holidays, paid sick leave.