Senior Staff Software Engineer, AI Platform

Rivian and Volkswagen Group Technologies•Palo Alto, CA

About The Position

Rivian and Volkswagen Group Technologies is a joint venture between two industry leaders with a clear vision for automotive’s next chapter. From operating systems to zonal controllers to cloud and connectivity solutions, we’re addressing the challenges of electric vehicles through technology that will set the standards for software-defined vehicles around the world. The road to the future is uncharted. By combining our expertise across connectivity, AI, security and more, we’ll map a new way forward. Working together, we’ll create a future that’s more connected, more intelligent, more sustainable for everyone. As a Senior Staff Software Engineer specializing in agentic applications, you will be the preeminent technical authority for our GenAI platform, setting architecture and strategy that spans multiple engineering organizations and shapes the company's long-term technology roadmap. You will lead the enterprise-wide integration of LLMs across our internal and customer-facing application portfolio. Your focus will be on driving cognitive automation at scale, transforming how the organization approaches decision-making and workflow automation, and establishing the engineering standards that define resilient, scalable, and observable distributed systems company-wide. You will operate as a force multiplier — influencing technical direction well beyond your immediate team and acting as a key partner to senior leadership in shaping the future of the platform.

Requirements

Bachelor's, Master's, or Ph.D. in Computer Science, Machine Learning, or a related field.
15+ years of hands-on experience in software engineering, with deep expertise in designing and building large-scale distributed systems.
Extensive, hands-on experience architecting and deploying complex agentic applications leveraging LLMs in production at enterprise scale.
Demonstrated track record of company-wide, and ideally industry-recognized, technical impact.
Expertise in Golang is strongly preferred.
Deep proficiency in Python or similar languages, along with expert-level knowledge of cloud and container frameworks such as AWS and Kubernetes.
A proven track record of architecting highly scalable, fault-tolerant, and observable distributed systems used across an entire organization.
Expert-level, authoritative grasp of machine learning principles, algorithms, and evaluation metrics, as well as software engineering best practices for distributed systems at scale.
Demonstrated success owning the design, deployment, and operation of mission-critical backend software in high-stakes production environments at company-wide organizational scale.
Exceptional communication and influencing skills, with a proven ability to build consensus among executive stakeholders, define engineering culture at the organizational level, and align the entire company on complex, high-stakes technical decisions.
A genuine and demonstrated passion for advancing the state of the art in machine learning and natural language processing, with a track record of translating cutting-edge research into durable business impact at scale.

Responsibilities

Architect and lead the development of highly sophisticated, mission-critical intelligent agent systems that utilize LLMs to automate workflows, optimize operations, and elevate user experiences — setting technical direction across the entire engineering organization and influencing strategy at the company level.
Own and drive the multi-year technical vision for integrating LLMs across the company's software ecosystem, ensuring robust, scalable, and maintainable communication and data exchange. Serve as the ultimate technical authority and final escalation point for LLM architecture decisions across the organization.
Partner directly with senior leadership, product executives, and engineering leaders to define and own the company-wide strategic direction for cognitive automation. Leverage LLMs to transform complex cognitive tasks — such as information extraction, summarization, and question answering — into scalable, repeatable capabilities across the enterprise.
Take ultimate technical ownership of the entire development lifecycle for the organization's most strategically important machine learning-powered tools and applications. Establish technical frameworks and architectural patterns that are adopted company-wide and that outlast individual projects or teams.
Set the engineering culture at the organizational level. Establish and enforce industry-leading standards for building production-grade, distributed machine learning solutions, and ensure consistent adoption across all engineering teams, not just your own.
Continuously research and experiment with emerging trends in machine learning, AI agents, and distributed systems. Translate findings into actionable technical strategy with measurable, company-wide impact, and represent the organization's technical point of view in industry and cross-company forums where relevant.
Work closely with machine learning engineers, product teams, and senior/executive leadership to gather requirements, define strategic scope, and deliver high-impact solutions. Own technical alignment across the entire organization and serve as the primary engineering representative at the executive level.
Mentor and elevate engineers at all levels, from mid-level to principal/staff, fostering a culture of technical excellence, innovation, and continuous learning across the company. Own hiring strategy and serve as the final technical bar-raiser for senior and staff-level hires.