• Lead and support engineering managers and their teams building platform systems for personalized recommendations across Spotify • Set and evolve the technical vision for foundational capabilities, including targeting, serving, evaluation, and agent-driven systems • Make thoughtful decisions about what should become platform capabilities, ensuring teams can move quickly without unnecessary complexity • Guide incremental platform evolution, enabling continuous delivery rather than large, disruptive rewrites • Partner closely with product, data science, and engineering leaders to align priorities across multiple squads • Stay close to the technology when needed, contributing to architecture decisions and resolving complex production challenges • Encourage adoption of AI-assisted development tools and shape how teams use them effectively in day-to-day work • Hire, develop, and grow engineering leaders and individual contributors, building a strong and inclusive team culture

• Audit, secure, and optimize our existing cloud infrastructure (AWS) to ensure high availability, fault tolerance, and security for both training and production workloads. • Design and maintain scalable architectures for serving deep learning models (PyTorch/TensorFlow), optimizing for low latency and high throughput in handling complex infrastructure data. • Build and maintain automated pipelines for model testing, validation, deployment, and rollback. • Architect efficient, scalable compute environments for training complex computer vision and time-series models on large datasets. • Implement comprehensive monitoring for model drift, data quality, and system health, ensuring rapid response to performance degradation.

• Design and implement end-to-end ML systems, including data ingestion, feature processing, model training, and model serving • Architect and deploy scalable AI services supporting real-time and batch inference use cases • Build and maintain ML infrastructure across cloud environments (e.g., EC2, EKS, SageMaker, specialized inference hardware) • Develop and evolve MLOps platforms, including training pipelines, deployment workflows, feature stores, and model observability • Implement CI/CD and infrastructure-as-code patterns to automate model lifecycle management • Optimize model training and inference performance for cost, latency, and hardware efficiency • Monitor production ML systems for accuracy, reliability, and operational health • Partner cross-functionally with data engineering, architecture, governance, and security teams to ensure compliant and scalable solutions • Mentor team members on ML engineering, system design, and operational best practices • Contribute to special initiatives that advance AI platform maturity and engineering standards

• Dataset ownership: define specs; audit and curate large-scale audio/text; close corpus gaps and fix sample-level issues. • Quality instrumentation: build automated gates/metrics (e.g., SNR, clipping, VAD, WER, SV/LID, safety) with dashboards; validate against listening tests. • Classifiers and filters: train lightweight models to tag, score, and filter data (VAD, ASR gating, LID, SV/diarization, noise/safety); calibrate to subjective outcomes. • Cleaning and integrity: apply denoise/dereverb/de-clip when beneficial; deduplicate and decontaminate; prevent leakage; maintain lineage and versioned releases. • Data selection: optimize mixtures via sampling, weighting, curriculum, and active learning; mine hard negatives and long-tail cases. • Tooling and pipelines: ship reproducible ETL and validation; integrate quality gates into training/eval; add monitoring and alerts. • Human-in-the-loop and compliance: run MTurk/vendor annotation with strong QC; ensure consent/licensing/policy compliance; collaborate across teams and document datasets.