• 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.

• Define and execute the strategic roadmap for generative ML research aligned with company objectives • Lead research initiatives in multimodal generative models (video, audio, language), temporal consistency techniques, and multimodal generation with main focus on video generation • Build, lead, and mentor a diverse, high-performing ML team • Partner with product, engineering, and creative teams to integrate ML innovations into production systems • Contribute to the company's overall technical and product strategy.

• Design, build, and deploy production-ready machine learning models that power critical features across our platform • Collaborate with your team to transform research prototypes into robust, scalable, and maintainable ML systems • Work closely with product and engineering teams to understand business problems and define technical ML solutions • Improve recommendation algorithms that personalize user experiences and drive engagement • Develop and maintain ML pipelines that can reliably process large volumes of data (LLM) • Monitor model performance in production and implement strategies to detect and address model drift • Participate in code reviews and mentor junior engineers in ML best practices • Stay current with the latest advancements and evaluate new techniques for potential application • Work on cutting edge projects that are pivotal to our Fortune 500 client