• Utilize in-house and 3rd party LLMs to solve language understanding problems • Employ techniques such as fine-tuning and RAG to improve models • Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development • Help drive optimization, testing, and tooling to improve quality of our content enrichment assets • Collaborate with cross-functional teams of MLEs, data and backend engineers, and other stakeholders including tech research, data science, and product to develop new features and technologies • Be a participant in our AI Foundation’s ML community and work collaboratively and efficiently within our existing platforms and systems • Perform data analysis to establish baselines and inform product decisions • Stay up-to-date on the latest machine learning algorithms and techniques

• Work closely with the Data Science team to develop and implement ML algorithms into production code. • Apply design and coding best practices, including vectorization and object-oriented programming in Python. • Ensure algorithms cover different scenarios with a focus on performance and scalability. • Rapid ramp-up to work on deliverables within Scrum teams. • Collaborate with cross-functional teams to integrate ML solutions into production systems. • Utilize CI/CD, version control (Git), and automated testing in ML projects.

• Deploy and maintain containerized workloads to support machine learning development, deployment, and post-deployment monitoring. • Utilize tools like helm and kustomize to accelerate the deployment of machine learning models and data pipelines. • Apply various optimization techniques such as compilation, quantization-aware-training (QAT), and pruning to improve latency and throughput of models. • Utilize open-source software like Metaflow, Prefect, Temporal, and Argo Workflows to facilitate data science development. • Build and optimize machine learning models to analyze security data, extract actionable insights, and identify trends, anomalies, and other relevant security signals. • Develop and maintain systems for drift detection and model monitoring to ensure continuous improvement and accuracy of insights. • Collaborate with cross-functional teams to design data pipelines that can efficiently process petabytes of raw internet security data.

• Lead BEV model development: Define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding). • Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations. • Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations. • Own large-scale training workflows — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization. • Advance model robustness and generalization, addressing long-tail conditions such as low visibility, occlusions, and rare scene configurations. • Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance. • Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces. • Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation. • Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception.