Who you are
You have deep experience building and deploying machine learning systems in production environments
You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms
You are proficient in Python and have hands-on experience building machine learning systems with PyTorch
You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA
You have worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization
You care about creating high-quality user experiences through thoughtful applications of machine learning
You communicate effectively with technical and non-technical partners and can influence technical decisions beyond your immediate team
You know how to design, execute, and interpret online experiments and A/B tests to improve user outcomes
You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks
You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms
What the job involves
Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience
Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally
Build content recommendation systems for emerging agentic and AI-powered user experiences
Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches
Partner closely with Product Managers, Engineers, Data Scientists, and Designers to define and execute experimentation strategies
Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency
Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale
Drive technical direction in ambiguous problem spaces and contribute to the long-term architecture of our personalization systems
Mentor and support other Machine Learning Engineers, sharing knowledge and helping strengthen engineering practices across the team
Benefits
Extensive learning opportunities, through our dedicated team, GreenHouse
Flexible share incentives letting you choose how you share in our success
Global parental leave, six months off - fully paid - for all new parents
All The Feels, our employee assistance program and self-care hub
Flexible public holidays, swap days off according to your values and beliefs