Director, AI Engineering (Data Science)

Blend is a premier AI services provider dedicated to co-creating meaningful impact through data science and AI. They are seeking a visionary Director of AI Engineering to lead the lifecycle of AI model development, ensuring innovative machine learning solutions are effectively implemented to drive business outcomes.

Responsibilities:

• Define technical investments with business objectives
• Mentor, and manage AI/ML engineers, senior data scientists, and MLOps engineers—setting performance expectations and a high-performance culture
• Partner with cross-functional leaders to prioritize initiatives, allocate resources, and measure organizational impact
• Establish engineering standards, code review practices, and model governance frameworks across the AI org
• Serve as the technical authority on deep learning architecture—personally leading the design and development of custom transformer models for sequence modeling, customer propensity scoring, audience segmentation, and churn prediction
• Drive innovation in attention mechanisms, positional encodings, and tokenization strategies specifically suited to tabular, time-series, and event-stream data common in marketing and telecom
• Oversee adaptation and fine-tuning of foundation models (BERT, T5, TabTransformer, LLMs) for proprietary client datasets, ensuring domain-specific performance
• Champion reproducible experimentation and architectural decision documentation across the team
• Oversee end-to-end data science workflows: problem framing, feature engineering, model development, validation, and production deployment
• Ensure statistical rigor in experimental design, causal inference, A/B testing, and offline/online evaluation frameworks
• Guide the team in building robust data pipelines for large-scale structured and unstructured datasets, including clickstream, CRM, ad telemetry, CDRs, and network KPIs
• Lead technical discovery and solutioning with enterprise clients translating ambiguous business problems into well-scoped AI initiatives
• Present AI strategy, model results, and roadmap updates to C-suite and senior client stakeholders with clarity and executive presence
• Contribute to business development: support RFP responses, lead technical portions of client proposals, and help grow the AI engineering practice
• Establish production standards for model deployment, monitoring, drift detection, and automated retraining across cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
• Drive adoption of MLOps best practices including CI/CD for ML, containerization (Docker/Kubernetes), and experiment tracking (MLflow, W&B, DVC)
• Implement model governance, explainability, and responsible AI standards in compliance with client and regulatory requirements

Requirements:

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a closely related quantitative field; Ph.D. strongly preferred
• 10+ years of progressive experience in data science and machine learning, with at least 3–5 years in a people management or technical leadership role (Director, Sr. Manager, or Principal Engineer level)
• Proven track record of leading high-performing AI/ML engineering teams in a fast-paced, client-facing or product environment
• Deep, hands-on expertise designing and training custom transformer architectures from scratch—not only fine-tuning pre-built checkpoints, but architecting novel attention mechanisms, embedding strategies, and model topologies
• Strong applied data science foundation: feature engineering, statistical modeling, causal inference, and experimental design across large-scale datasets
• Proficiency in Python and core ML/DL libraries: PyTorch (preferred), TensorFlow, HuggingFace Transformers, scikit-learn, XGBoost/LightGBM
• Direct experience with industry datasets in marketing & media (DSP/DMP logs, ad impression data, attribution pipelines, MMM) OR telecommunications (CDRs, network KPIs, subscriber behavior, churn datasets)
• Command of SQL and large-scale data platforms: Spark, BigQuery, Snowflake, or Databricks
• Experience owning end-to-end MLOps: cloud deployment (SageMaker, Vertex AI, or Azure ML), monitoring, CI/CD for ML, and model governance
• Exceptional executive communication skills—able to translate complex model behavior into business language for C-suite and client audiences
• Professional services experience across multiple client engagements or business units
• Background in privacy-preserving ML: federated learning, differential privacy, or synthetic data generation—especially relevant in post-cookie marketing environments
• Knowledge of graph neural networks (GNNs) for social graph or network topology analysis in telecom contexts
• Published research or conference contributions (NeurIPS, ICML, KDD, RecSys, or industry equivalents) related to applied transformers, tabular deep learning, or domain-specific AI
• Experience with real-time inference and streaming ML pipelines (Kafka, Flink, or similar)
• Demonstrated ability to build strategic partnerships with external clients, contributing to revenue growth or account expansion through technical leadership
• Deep experience with openai focused on embeddings
• Experience building custom transformer models