Descripción
This role is categorized as hybrid. This means the successful candidate is expected to report to offices in Austin, TX, Mountain View, CA or the Greater Seattle Area three times per week, at minimum [or other frequency dictated by the business if more than 3 days].
The Role
As an engineer on this team, you will be responsible for building and supporting a petabyte-scale data platform in the cloud and providing powerful foundations for GM ML Data Platform tools, frameworks, and services. Responsibilities include ensuring scalable, transparent, and reliable data ingestion and management; development of fast, robust, and spike-resistant data consumption, data mining, and processing tools for the entire company; and development of orchestration for large-scale post-processing, and computational pipelines.
What You’ll Do
- Lead us in the development, optimization and productionization of the next generation ML data processing platform using Beam and Spark in the cloud.
- Build self-serve capabilities to help customers to adopt the next generation ML data processing platform
- Use the latest cloud technologies to own, design, implement, and test scalable distributed data systems in the cloud. Champion engineering excellence by continuously improving systems and processes
- Own technical projects from start to finish, contribute to the team’s product roadmap, and be responsible for major technical decisions and tradeoffs. Effectively participate in team’s planning, code reviews and design discussions
- Consider the effects of projects across multiple teams and proactively manage conflicts. Work together with partner teams and orgs to achieve cross-organizational goals and satisfy broad requirements
- Conduct technical interviews with well-calibrated standards and play an essential role in recruiting activities. Effectively onboard and mentor junior engineers and/or interns
[Additional Description]
Your Skills & Abilities (Required Qualifications)
- 10+ years working with big data
- Bachelor's in Computer Science, Electrical Engineering, Mathematics, Physics, or another relevant field; or equivalent real-world experience
- Experience building ML data processing systems using Beam / Spark and its ecosystems from the ground up.
- Experience optimizing those data processing clusters for cost efficiency and performance
- Experience building serving systems capable of delivering data at high-throughput, low-latency and high QPS in a cost-efficient and spike-resilient manner.
- Experience building full ML model lifecycle solutions - from feature engineering to training, validation, deployment and monitoring.
- Experience building scalable infrastructure on the cloud with Python or Java/Scala (or similar)
- Passionate about self-driving technology and its potential impact on the world
- Attention to detail and a passion for seeking truth
- A track record of efficiently solving complex problems
- Startup mentality - openness to dealing with unknown unknowns and wearing many hats
What Can Give You a Competitive Advantage (Preferred Qualifications)
- MS or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Physics, or another relevant field
- Demonstrable expertise in a building end-to-end data ingestion, processing and serving systems at petabyte scale from the ground up
- Proficiency in writing SQL queries for analytic purposes
- Relevant publications
This job may be eligible for relocation benefits.
Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.
Compensation:
- The expected base compensation for this role is: $165,000 - $298,800. Actual base compensation within the identified range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
- Benefits : GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
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Información sobre diversidad
General Motors se compromete a ser un lugar de trabajo en el cual no solo no haya discriminación indebida, sino que fomente con sinceridad la inclusión y el sentido de pertenencia. Creemos firmemente que la diversidad del personal crea un entorno en el cual nuestros empleados pueden prosperar y desarrollar mejores productos para nuestros clientes. Instamos a los candidatos interesados a que revisen las responsabilidades y aptitudes clave para cada puesto y se postulen para los puestos que coincidan con sus habilidades y capacidades. Es posible que, cuando corresponda, se les pida a los solicitantes que están en el proceso de contratación que completen satisfactoriamente una o más evaluaciones relacionadas con su función y/o una evaluación previa al empleo antes de comenzar a trabajar. Para obtener más información, visite Cómo contratamos.
Declaración de igualdad de oportunidades en el empleo (EE.UU.)
General Motors se enorgullece de ser un empleador que ofrece igualdad de oportunidades. Todos los solicitantes calificados serán tenidos en cuenta para el empleo sin distinción de raza, color, religión, sexo, orientación sexual, identidad de género, nacionalidad, discapacidad o condición de veterano protegido.
Adecuaciones (EE.UU. y Canadá)
General Motors ofrece oportunidades a todos los solicitantes de empleo, incluyendo las personas con discapacidades. Si necesita una adecuación razonable para ayudarle con su búsqueda o solicitud de empleo, envíenos un correo electrónico a [email protected] o llámenos al 800-865-7580. En su correo electrónico, incluya una descripción del puesto específico que está solicitando, así como el título del empleo y el número de solicitud del puesto que está solicitando.