• Innovate: Stay at the forefront of AI/ML advancements, continuously exploring new techniques, models, and tools. Your insights will help drive innovation and keep our platform at the cutting edge of technology. • Collaborate: Work closely with core platform and automation engineers, as well as other stakeholders, to identify and address business needs through Machine Learning solutions. You will play a key role in bridging the gap between AI research and practical application. • Integrate: Utilize APIs, pre-trained models, and other tools to implement AI-driven features across our platform. You will focus on integrating these capabilities into production systems, enhancing the functionality and performance of our solutions. • Optimize: Fine-tune and optimize existing solutions for specific product requirements, ensuring that AI integrations are efficient, scalable, and reliable.

• Build, train, and refine machine learning and deep learning models using time-series, sensor, and behavioral data. • Integrate data from wearables, fitness tracking platforms, and device APIs to create a clear story from movement, patterns, and activity signals. • Develop and maintain data pipelines that support both batch and real-time analytics. • Own model deployment in production environments — your models won’t live in notebooks; they’ll live in the world. • Work closely with engineering teams to integrate ML models into mobile and web apps. • Support logic for fraud, spoofing, and anomaly detection, ensuring data reflects real human activity. • Make complex outputs easy to understand — not just for engineers, but for product and business users too.

• Set technical strategy and drive a multi-quarter roadmap for ML platform capabilities aligned to Shippo’s business priorities. • Own cross-team architecture decisions, RFCs, and design reviews for ML lifecycle and inference. • Raise the engineering bar through mentorship, production readiness standards, and reusable platform primitives. • Be accountable for platform adoption, reliability, and cost-performance outcomes. • Build and operate core ML platform components: ML lifecycle foundation (experiment tracking, reproducibility, artifact management, model registry, versioning, and controlled promotion workflows using MLflow or equivalent). • Training and experimentation enablement (standardized environments, reusable pipelines/templates, evaluation harnesses, and repeatable workflows that let data scientists move from exploration to production with confidence). • Kubernetes-native model serving for real-time inference (safe rollout and rollback, autoscaling, reliability practices, and cost controls). • Batch inference and scoring pipelines (repeatable backfills, retraining triggers, consistent packaging between training and inference). • Observability for ML systems (service health metrics, alerting, and model-quality signals such as drift and data quality). • Developer experience (templates, reference implementations, documentation, and self-service workflows). • Evaluate and recommend inference frameworks and deployment patterns, and document tradeoffs for Shippo’s workloads. • Identify and resolve performance bottlenecks across the inference stack (model runtime, compute utilization, networking, serialization, and autoscaling behavior). • Establish ML engineering standards across training, evaluation, testing, model packaging, CI/CD, production readiness, and incident response. • Partner with Data Science teams to bridge research and production environments by creating repeatable frameworks, shared standards for code quality and reproducibility, and self-serve paths to deploy models safely. • Collaborate with Data and Engineering teams to ensure the platform supports real workflows, drives adoption, and meets reliability expectations. • Mentor engineers through design reviews, architecture guidance, and shared best practices across platform and ML development.

• Research and build deep learning systems that can generate expressive, natural-sounding speech from text or audio prompts. • Collaborate with cross-functional teams to integrate your work into production-ready pipelines. • Help build state-of-the-art generative systems for video and audio synthesis, performance transfer, and visual translation. • Shape core infrastructure, set best practices, communicate with stakeholders, and mentor junior engineers while delivering high-quality ML pipelines in production environments.