About Ghost

At Ghost, our mission is to make self-driving for everyone. We build autonomous driving software for automakers, based on a breakthrough in artificial intelligence that finally makes highway autonomy safe and scalable for the consumer car market.

Ghost helps automakers reimagine the car of the future with a complete autonomy solution that can be fully customized and continuously upgraded, delivering a car that keeps getting better year after year.

At Ghost, we are responsible for both invention and productization – not only solving complex problems with novel technology but making sure that it can scale to millions of drivers on the road. It’s a bold undertaking, but one that makes for constant learning, real-world impact, and fulfilling work. Together, we are a small, multi-disciplinary team tackling one of the hardest challenges in technology today.

Ghost was founded in 2017 by John Hayes and Volkmar Uhlig. Before Ghost, John co-founded Pure Storage, taking the company public in 2015.

Ghost has over a hundred employees across its headquarters in Mountain View and additional offices in Dallas, Detroit, and Sydney. Ghost has raised over $200 million from investors including Mike Speiser at Sutter Hill Ventures, Keith Rabois at Founders Fund, and Vinod Khosla at Khosla Ventures.

Learn more at https://ghostautonomy.com.

The Role

As an engineer at Ghost, you will help us build the attention-free self-driving platform for a new generation of consumer cars. Achieving this requires application of almost every computer science discipline, giving you an opportunity to work on a variety of interesting problems while contributing to an exciting goal.

You will be working in our Sydney office as part of a global team building automotive-grade hardware and software. The broad spectrum of work ranges from developing languages and compilers to optimize coding for performance to building reliable and fault-tolerant drivers, operating systems, and run-times.

You will be involved in active research and development, turning them into product in an iterative, fast paced environment in which you will see continuous impact of your work.

What you will do:

• Design software that runs on embedded systems in autonomous vehicles and in data centers such as: Fault-tolerant distributed runtime systems for co-ordination and execution of parts of autonomous driving system
• Domain-specific language for expressing aspects of the driving system
• Distributed database systems for processing, querying, analyzing, and engineering/validating models based on recorded video and vehicle sensor data
• Execute mission-critical systems engineering work including, but not limited to: autonomous driving runtime, operating systems, embedded systems, large-scale distributed databases, model training systems, performance optimization, and software/hardware system architecture
• Utilize vector databases for GPT models in text, video and radar domains
• (For Tech Leads and Managers) Oversee significant cross-functional efforts while collaborating with other senior leaders across Ghost
• (For Tech Leads and Managers) Supervise project planning and execution for a team while keeping people motivated and engaged
• (For Managers) Spearhead recruitment, people development, and retention of team members

Requirements:

• BS/MS/PhD in Computer Science or related field
• 5+ years of software development experience
• Deep understanding of OS, computer architecture, and distributed systems
• Core competency in software engineering best practices – version control, requirements gathering, debugging, and testing
• (For Tech Leads and Managers) Experience leading teams to successfully complete complex software projects
• (For Managers) 3+ years of direct people management experience

Nice to haves:

• Low level platform bring-up, device drivers, and embedded operating systems
• Complex systems software and user-level runtime
• Building efficient protocols for secure and performant data transfer
• Processing and visualizing large datasets efficiently, using tools such as Python, numpy, and Jupyter notebooks
• Microcontrollers, device drivers, and operating systems
• Languages and compiler development
• Machine learning and neural networks
• GPU accelerated computing and libraries such as OpenCL, including an understanding of how to write performant programs accelerated by GPUs
• Advanced mathematics, particularly linear algebra