About the job
We’re looking for a Senior Machine Learning Engineer to help build the next generation of tools and platforms that power high-impact decisions across Siri. In this role, you'll operate at the intersection of engineering excellence and business impact. You’ll design and implement scalable, reliable systems to transform raw data into actionable insights for leadership. This is a high-visibility, high-impact position with the opportunity to influence the direction of products and strategy
Responsibilities
You will drive the technical vision for Siri’s automated anomaly detection platform for detecting performance and reliability regressions. Define, develop and deliver key features for high quality alerting to enable teams to troubleshoot regressions rapidly. Technically represent the team and communicate progress on key deliverables across the organization from peer groups to senior leadership. Own the technical roadmap, onboard and mentor team members, and lead the team to deliver high-impact outcomes. Execute in a rapidly changing environment with ambiguous requirements to drive impact incrementally. Demonstrate strong problem solving skills and be self-directed with a proven ability to execute. Continually desire learning and demonstrate attention to details and find opportunities to innovate and share knowledge with others.
Qualifications
Minimum
Master’s degree with 8+ years of industry experience in machine learning, or Ph.D. with 5+ years, applying ML to real-world business problems.
Strong understanding of core ML concepts, with particular depth in unsupervised learning methods (clustering, dimensionality reduction, density estimation), and a solid foundation in feature engineering, model evaluation, regularization, and optimization.
Advanced coding skills in Python (5+ years) with pandas, scikit-learn, and at least one deep learning framework (PyTorch or TensorFlow).
Hands-on experience data preprocessing, building and training ML models using distributed processing frameworks such as PySpark, Spark, or Flink.
Experience applying large language models (LLMs) for downstream tasks (classification, labeling, enrichment), with the ability to perform fine-tuning or parameter-efficient adaptation (e.g., LoRA). Must be capable of deploying and optimizing models in on-premise, server, or on-device environments, rather than relying solely on hosted third-party APIs
Demonstrated ability to set technical vision, lead complex projects, and drive impact in cross-functional environments, with strong communication and problem-solving skills.
Preferred
Proven expertise with anomaly detection and time series modeling (e.g., Isolation Forest, autoencoders, ARIMA, LSTM) and experience building production frameworks supporting multiple engineering and product teams.
Experience with LLM workflows (domain adaptation, RAG) and deploying optimized ML/LLM models on mobile or server environments (e.g., Core ML, TensorFlow Lite, ONNX Runtime) for performance, cost, and privacy.
Experience in developing ML infrastructure, and large-scale operations, including model serving, distributed training, CI/CD for ML pipelines, and platform monitoring across millions of devices or events.
Familiarity with composite metrics and interpretability tools (e.g., SHAP, LIME), with a track record of publications, patents, or open-source contributions in ML/LLMs, anomaly detection, or time series modeling.