Staff Machine Learning Engineer, Offline Infrastructure

OverviewThe opportunity Unity Vector builds an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company. Our systems operate at scale across batch and streaming data, supporting analytics, product intelligence, machine learning pipelines, and business operations. As data volume and complexity grow, our platform also supports large-scale model training, feature generation, and experimentation workflows that power production ML systems. To support this growth, we need strong technical ownership to ensure our ML pipelines remain reliable, scalable, and architecturally sound. We are seeking a staff ML engineer to design and evolve the large-scale offline platform. This role focuses on building reliable infrastructure for generating training datasets, orchestrating ML workflows, and enabling efficient, distributed model training at scale. You will work closely with ML engineers and platform teams to ensure our pipelines can efficiently handle growing data volumes and increasingly complex training workloads. You will play a key role in shaping how model datasets are prepared as well as model training, validated, and delivered to distributed training systems, while ensuring the reliability, scalability, and performance of our offline ML platform.What you'll be doingDesign and operate large-scale data pipelines that generate training datasets used for machine learning training and experimentationDevelop infrastructure that supports distributed training workflows using technologies such as PyTorch, Ray Data, and Ray Train, etc.Integrate ML pipelines with workflow orchestration systems (e.g., Flyte, Airflow, or similar) to enable reliable multi-stage training workflowsImprove reproducibility and observability of ML pipelines through dataset validation, monitoring, and automated testingOptimize performance and resource utilization across distributed compute systems used for data processing and model trainingPartner closely with ML engineers to enable efficient large-scale experimentation and model iterationLead architectural improvements to ensure our offline ML pipelines remain scalable, reliable, and cost-efficientWhat we're looking forStrong experience building large-scale ML pipelinesExperience working with distributed computing frameworks such as Ray, Spark, Flink and familiarity in the Ray ecosystem (Ray Data, Ray Train) for distributed data processing and model trainingExperience building infrastructure for training data generation, dataset preparation, or ML feature pipelinesDeep experience designing and operating production-grade data pipelinesStrong programming skills in Python and experience working with large-scale distributed workloadsExperience with modern data infrastructure (data lakes, warehouses, orchestration systems, streaming platforms)Strong systems thinking, with the ability to reason about performance, scalability, reliability, and cost tradeoffs in distributed systemsProven ability to lead technical direction and influence architectural decisions across teams without formal authorityNoteRelocation support is not available for this position.J-18808-Ljbffr