Senior Machine Learning Ops Engineer, GenAI at Rivian and VW Group Technology

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

23 Jan, 26

Salary

203500.0

Posted On

25 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, ML Ops, Databricks, Large Language Models, Distributed Training, Torch Distributed, Ray, Kubeflow, MLflow, Cloud Technologies, Kubernetes, AWS, GCP, Azure, Python, Problem-Solving

Industry

Software Development

Description

About Us 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. Role Summary As an ML Ops Engineer, you will be instrumental in building and maintaining a scalable training and inference platform using both Databricks and open-source technologies. Your role will focus on managing the ML/AI model life cycles in production, including running Large Language Models (LLMs) on bare metal GPUs. You will work with distributed training frameworks and cloud technologies to ensure robust and efficient ML operations. Responsibilities Develop Scalable ML Infrastructure: Design and implement a scalable training and inference platform using Databricks and open-source technologies to support ML/AI solutions. Manage Model Life Cycles: Oversee the end-to-end life cycle of ML/AI models in production, ensuring efficient deployment, monitoring, and maintenance. Run LLMs on Bare Metal GPUs: Optimize and manage the execution of Large Language Models on bare metal GPUs to enhance performance and scalability. Utilize Distributed Training Frameworks: Leverage distributed training frameworks such as Torch Distributed and Ray to improve training efficiency and model performance. Implement ML Frameworks: Work with frameworks like Kubeflow, MLflow, Argent, and Weights & Biases to streamline ML operations and model management. Leverage Cloud Technologies: Utilize cloud platforms such as Kubernetes, AWS, GCP, and Azure to build and manage scalable ML infrastructure. Collaborate with Cross-Functional Teams: Work closely with data scientists, software engineers, and other stakeholders to integrate ML solutions into existing systems and workflows. Establish Best Practices: Define and implement best practices for ML Ops, ensuring scalability, reliability, and maintainability of ML solutions. Stay Informed on Industry Trends: Continuously research and incorporate emerging trends and technologies in ML Ops and infrastructure to enhance our capabilities. Qualifications Educational Background: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven Experience: 5+ years of experience in ML Ops, infrastructure, or related fields, with a focus on managing ML/AI models in production. Technical Expertise: Proficiency in distributed training frameworks (Torch Distributed, Ray) and ML frameworks (Kubeflow, MLflow, Argent, Weights & Biases). Cloud Proficiency: Strong experience with cloud technologies, including Kubernetes and major cloud providers (AWS, GCP, Azure). Programming Skills: Expertise in programming languages such as Python, and familiarity with ML libraries and tools. Problem-Solving Skills: Strong analytical and problem-solving skills, with the ability to troubleshoot complex ML infrastructure issues. Collaborative Mindset: Excellent communication and teamwork skills, with the ability to work effectively in a cross-functional team environment. Passion for Innovation: A keen interest in exploring and applying the latest advancements in ML Ops and infrastructure to drive innovation. Pay Disclosure Salary Range/Hourly Rate for California Based Applicants: $162,800.00 - $203,500.00 (actual compensation will be determined based on experience, location, and other factors permitted by law) Benefits Summary: Rivian and Volkswagen Group Technologies provides robust medical/Rx, dental and vision insurance packages for full-time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment, and Rivian covers most of the premiums. Equal Opportunity Rivian and Volkswagen Group Technologies is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law. We are also committed to ensuring compliance with all applicable fair employment practice laws regarding citizenship and immigration status. Rivian and Volkswagen Group Technologies is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at candidateaccommodations@rivian.com. Candidate Data Privacy Rivian and VW Group Technologies (“Rivian and Volkswagen Group Technologies”) may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Rivian and Volkswagen Group Technologies may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law. Rivian and Volkswagen Group Technologies may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian and Volkswagen Group Technologies affiliates; and (iii) Rivian and Volkswagen Group Technologies’ service providers, including providers of background checks, staffing services, and cloud services. Rivian and Volkswagen Group Technologies may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions. Please see our Candidate Data Privacy Notice (English) and Candidate Data Privacy Notice (Serbian) for more information. Please note that we are currently not accepting applications from third party application services.

Responsibilities

Build and maintain a scalable training and inference platform using Databricks and open-source technologies. Manage the ML/AI model life cycles in production, ensuring efficient deployment and monitoring.