AI Data Engineer Jobs

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Ship critical infrastructure managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding the company's value to customers, pushing back on requirements if a better, faster solution is seen. Demonstrate full-stack proficiency by working across system boundaries, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate so focus can be on quality, architecture, and product taste. Constantly raise the velocity bar by optimizing development loops, refactoring legacy patterns, automating workflows, and fixing broken processes.

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Oversee the end-to-end lifecycle of data acquisition and management for foundation models across 3D, video, image, and audio. Identify and acquire diverse datasets from public and commercial partners while managing complex technical and legal requirements. Collaborate with research teams to ensure data sources align with specific model training and fine-tuning needs. Manage technical lifecycle of large-scale data, including ingestion, curation, and AWS S3 storage optimization, ensuring system reliability and code quality. Develop internal tools and standards to make datasets searchable, accessible, and efficiently indexed for research. Partner with legal team to mitigate risks, ensure global regulatory compliance, and manage sensitive data protection rules. Represent the company in legal matters including providing testimony regarding data usage and licensing. Lead data vendor management by negotiating Master Services Agreements and Statements of Work, oversee partnerships for data annotation, evaluation, and collection projects. Drive cross-functional alignment between technical leads and researchers to ensure data strategy supports the company product roadmap.

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Design and build robust connectors across SQL/NoSQL databases, APIs (REST/GraphQL), and SaaS platforms such as CRM and storage systems. Interpret and model heterogeneous source schemas. Transform raw source data into formats optimized for AI inference. Collaborate closely with ML, applied AI, and forward deployed teams to define feature expectations. Work with infrastructure teams to design and ship hosted data pipelines. Optimize for latency, consistency, and edge constraints. Design resilient ingestion patterns for unreliable or rate-limited systems. Build logging, monitoring, and debuggability into all integrations.

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The Signal Engineer will build pipelines that ingest, clean, deduplicate, filter, and score training data at terabyte to petabyte scale. They will develop quality classifiers and heuristics to separate useful data from the rest, design dataset mixtures, and conduct experiments to determine what improves the model. The engineer will also create tools to explore, sample, and audit the corpus, and work closely with researchers and training engineers to ensure data choices connect to model behavior.

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The role involves building and operating the next generation of data infrastructure at Mistral AI, being a core contributor to the design and scaling of massive compute fleets and storage systems for high performance and scalability. Responsibilities include architecting and maintaining multi-cluster orchestration layers for optimizing workload placement across diverse hardware and regions, designing future-proof storage systems anticipating exabyte scale growth, contributing to the internal training platform development to support model training and fine-tuning across Kubernetes and SLURM environments, implementing and managing metadata and lineage systems to provide visibility and traceability of data and model pipelines, and managing cloud-native deployments using modern workflows to ensure scalability and operational excellence. The role also includes full lifecycle ownership, from migrating away from legacy orchestrators to implementing production-grade pipelines and participating in on-call rotations for critical training jobs.

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The Agentic Finance Engineer is responsible for designing, building, and maintaining a reliable financial data foundation using modern tools, covering revenue, AP/AR, procurement, close, strategic finance, and FP&A. They will partner closely with the data infrastructure team to build the financial data model, define canonical datasets, dimensional schemas, and transformation logic for Finance stakeholders. This role includes partnering with Finance leads to translate business requirements into technical architecture, building and maintaining dashboards and self-serve reporting tools to provide real-time visibility into key metrics. The engineer will own the Agentic Finance roadmap, prioritize use cases, and drive features from ideation to deployment, identifying high-value automation opportunities across Finance and corporate operations, and shipping solutions to eliminate manual work. They will build intelligent, reliable automation using agents, AI-powered tools, multi-step ETL jobs, and internal tooling that Finance teams use, such as lightweight apps, workflow automations, and AI-assisted processes. The engineer must enforce data integrity standards and testing practices to ensure auditability and reliability, ensure AI-assisted processes meet governance and controls standards with clear auditability, and champion a culture of data quality and documentation so that Finance teams trust and rely on the systems built.

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The Senior Data Intelligence Engineer is responsible for building and maintaining high-fidelity dbt and SQL models that serve as the foundational data for complex, usage-based revenue models. They develop tools and permissions frameworks enabling 'Analyst Agents' to query data sources such as Athena, correlate Salesforce churn signals, and identify API latency issues. The engineer acts as the technical liaison with the Engineering/Infrastructure team to ensure data contracts are reliable and ready for autonomous agents. They partner with the Head of Data to ingest and transform thousands of hours of unstructured internal call audio into queryable insights for go-to-market teams using Deepgram’s own models. The role includes maintaining a culture focused on automating manual and repetitive SQL tasks through code and agent systems rather than legacy dashboards.

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The Tech Lead Manager, Data Infrastructure at Cartesia is responsible for defining the overall multi-modal data strategy across pre-training and post-training, including human, synthetic, and web-scale data sources. They lead, manage, and mentor a team of data engineers and specialists. They design and oversee the construction of robust, scalable data pipelines for text, audio, and video, establish and enforce rigorous standards for data quality across the organization, deeply understand how data affects model capability and proactively identify and source novel datasets, and manage relationships and budgets with external data vendors and partners.

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Build and operate infrastructure for research and inference including distributed training, 1000+ Kubernetes GPU clusters, and petabyte-scale data pipelines. Design multi-stage pipelines converting petabytes of raw data into clean, annotated datasets. Run classification models on billions of images. Deploy and combine large language models to caption massive multimedia data. Manage distributed training and inference on GPU Kubernetes clusters. Solve orchestration and scaling challenges for large-scale GPU job processing. Scale workloads and research between clusters in multiple datacenters. Profile and optimize dataloaders streaming thousands of images per second. Profile and debug InfiniBand networking on huge training runs. Build fault tolerance systems for large-scale pretraining. Collaborate with researchers on evolving reinforcement learning infrastructure. Find clean scenes in millions of videos using distributed shot-boundary detection. Customize and train models to filter billions of images. Build systems bridging raw cluster capacity and research output.

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The Senior Data Engineer at HackerOne is responsible for leading the end-to-end design and delivery of scalable, secure, and intelligent data products and solutions to support the company's transformation into an AI-first organization. This role involves partnering across business and engineering teams to identify opportunities for automation, integration, and system modernization, driving the architecture and execution of platform-level capabilities by leveraging AI and modern tooling to reduce manual effort, improve decision-making, and increase system resilience. The engineer will provide technical leadership to internal engineers and external development partners to ensure design quality, operational excellence, and long-term maintainability, shape and contribute to incident and on-call response strategies, playbooks, and processes to build systems that fail gracefully and recover quickly, mentor other engineers and advocate for technical excellence, and promote a culture of innovation and continuous improvement. Additionally, the role includes championing effective change management to ensure systems are successfully launched, adopted, understood, and evolved.