We are seeking a hands-on Data Engineering Team Leader to oversee our real-time data domain and lead the evolution of our data platform as it transitions from a heavy build-out to stability, quality, and scale.

This role is responsible for maintaining, hardening, and extending our event processing infrastructure, focusing on adding targeted real-time processing capabilities to support analytics and informed decision-making, our decisions are deeply data-driven. This critical role is centered on maintaining, hardening, and extending our event processing infrastructure, with a primary focus on adding targeted real-time processing capabilities. Your work will directly support our analytics and enable informed, data-driven business decision-making based on production data. Specifically, you will be responsible for the core data component of capturing the clickstream events from the website, which is the foundational functionality for all our analytics.

Looking forward, this role will help lead the team into its next phase: improving data quality and developer experience, investing in modern table formats (Iceberg), empowering analysts and data scientists, and continuously reducing cost and operational friction.

This is a player-coach role, combining hands-on technical leadership with ownership, prioritization, and mentorship.

What You'll Do
Own the end-to-end real-time and event data domain, including ingestion, processing, and downstream consumption.

Ensure stability, correctness, and observability of existing streaming and near-real-time pipelines.
Lead the design and implementation of select real-time processing components to support analytics and decision-making use cases.
Define clear ownership, SLAs, and best practices for event data usage across the company.
Lead the teams investment in modern data infrastructure
Drive architectural decisions with a long-term view on maintainability, cost, and scalability.
Team Leadership & Execution

Lead, mentor, and support a team of data engineers; set technical standards and review designs and code.
Act as a hands-on contributor in critical areas of the system.
Own planning and prioritization, balancing new investments with platform stability and technical debt.
Promote a culture of quality, documentation, and operational excellence.
Partner with Data Analytics and Data Science teams to improve data accessibility, trust, and self-service capabilities.
Improve observability across the data platform (data quality, freshness, lineage, failures).
Explore and adopt AI-assisted development tools to improve engineering velocity and code quality.
Stay current with emerging technologies in data engineering and analytics, and evaluate their relevance to the platform.

We are looking for a talented and self-motivated Annotation Specialist to join our Annotation team and work closely with our Algorithms department.
This is a unique opportunity to make a real impact in a meaningful, safety-critical industry.
Responsibilities:
Be responsible for the ground truth annotation and tagging and channel accurate visual data to the deep learning pipeline.
Work with various imagery sensors such as RGB, Thermal and visual geographical data.
Work closely with internal teams to understand their requirements for annotated data.
Suggest methods for improving work efficiency.

We are looking for a Principal Engineer to lead the architectural vision of the platforms core. In this role, you will be the internal technical authority responsible for building a unified, high-performance engine that processes massive telemetry streams and runs advanced predictive models, regardless of where the infrastructure resides.

What youll be doing:
Unified Architectural Vision: Lead the design of a flexible, high-scale architecture that supports both multi-tenant SaaS environments and complex on-premises deployments.
Operationalizing Predictive Models: Bridge the gap between AI research and production by architecting the framework that runs sophisticated predictive algorithms at scale, ensuring they are robust enough for mission-critical environments.
High-Scale Engineering: Design distributed systems to handle the extreme telemetry density of large-scale AI clusters, ensuring efficient data ingestion, processing, and real-time analysis.
Cross-Organizational Leadership: Collaborate with networking and infrastructure teams to define the technical standards that enable the AIOps platform to integrate seamlessly with global AI infrastructure.
Technical Excellence: Drive the engineering roadmap, mentor senior staff, and serve as the final authority on architectural decisions, ensuring the platform meets the highest standards of reliability and scalability.

We are looking for an outstanding Senior Technical instructor to join our Education Services team. In this role, you will provide advanced training on our data center, AI, and HPC platforms to customers and partners around the world. You will also contribute to developing training content, including presentation decks, hands-on manuals, and supporting materials. You will help engineers, architects, and operations teams build the skills needed to design, deploy, and operate our-powered AI data centers with confidence. As part of this, you will take part in the design of hands-on workshops and lab environments that simulate real systems and data center components, enabling learners to practice on realistic end-to-end scenarios.

What youll be doing:

Deliver in-person and remote technical training on our data center, AI, and HPC platforms for customers and partners, with up to 25% travel as needed.

Lead lab-based training sessions that walk learners through end-to-end workflows on our GPUs, DGX/GB200 systems, high-speed networking, and AI/HPC software stacks.

Collaborate with internal experts and content developers to build agendas, exercises, and demos that reflect real customer use cases and guidelines.

Serve as a subject-matter guide (SME) for a dedicated production team and learning developers. They build primary content. Partner with them on slideware, lab guides, videos, simulations, visual demos, and exercises. Ensure technical accuracy and realism.

Act as a technical ambassador for us in front of partner and customer audiences, answering inquiries, facilitating discussions, and guiding learners through fix and what-if scenarios.

We are looking for an experienced Software Engineer to join our team with a focus on services and infrastructure development, while being comfortable contributing across the full software stack when required.

This role is well suited to an engineer who enjoys designing and building scalable backend services, integrating them with infrastructure platforms, and contributing to end-to-end solutions that support sophisticated engineering workflows. While some components may include frontend elements, the primary emphasis is on service architecture, APIs, data handling, and system reliability.

In this role, you will collaborate closely with engineers and partners across the organization to design, deliver, and evolve maintainable solutions that underpin GPU software engineering infrastructure. You will also contribute to technical direction, mentor others, and help shape the long-term evolution of our platforms.

Responsibilities:
Design, develop, and maintain scalable backend services using Python, Go or similar languages, with the ability to contribute to full-stack solutions where appropriate.
Take ownership of services across their lifecycle, from design and implementation through deployment and production support.
Design and evolve APIs and service interfaces that support tooling, automation, and infrastructure workflows.
Contribute to end-to-end feature development, spanning backend services, APIs, and selective frontend components where they support infrastructure use cases.
Collaborate closely with teams across tooling, automation, and infrastructure to ensure seamless service integration.
Improve system reliability, performance, and scalability through thoughtful design and continuous optimization.
Contribute to infrastructure modernization initiatives, including cloud-based and containerized deployments.
Follow and promote standard methodologies for code quality, testing, documentation, and design reviews.