Senior Machine Learning Engineer – Distributed Optimization Engineer

1 Month ago • 6 Years + • Artificial Intelligence

About the job

Job Description

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Our group's mission is to design, prototype, and productionize novel methods, tools, and infrastructure for efficient modeling and simulations of complex systems at scale. These tools aid in measuring the safety of our AI and readiness for launch. We use recent advances in machine learning, optimization, and numerical methods to verify/validate our software and AI in the most challenging environments, and subsequently develop tools to enable other teams at Zoox to do the same.

We are looking for experienced software engineers who can design, implement, and maintain large-scale high-performance machine learning systems. In this role, you will be responsible for architecting and scaling the core capabilities of our platform. You will work cross-functionally with engineers in AI, simulation, ML platform, and infra to push the boundaries of scalability and performance, and bring state-of-the-art machine learning and optimization applications to production.

In this role, you will:

    • Build the foundational blocks of our machine learning engine used for simulations, and improve its core capabilities
    • Design and support large-scale high-performance infrastructure services
    • Lead complex cross-functional projects
    • Keep up with the state-of-the-art distributed systems and AI frameworks
    • Communicate your work to other teams at Zoox

Qualifications:

    • 6+ years of industry experience in architecting and scaling distributed systems in production
    • Experience building high-performance machine learning workflows
    • BS/MS in Computer Science or equivalent experience
    • Fluency in production-level Python
Compensation
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. The salary range for this position is $196,000 to $246,000. A sign-on bonus may be offered as part of the compensation package. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
 
Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.


About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.


Accommodations
If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.

A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
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About The Company

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.

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Foster City, California, United States (On-Site)

Foster City, California, United States (On-Site)

Foster City, California, United States (On-Site)

Foster City, California, United States (On-Site)

Foster City, California, United States (On-Site)

San Diego, California, United States (Hybrid)

Foster City, California, United States (Hybrid)

Foster City, California, United States (On-Site)

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