Make a game-changing next move.

Learn more about the opportunities in Coatue's portfolio.

Machine Learning Systems (MLS) Engineer



Software Engineering
United States
Posted on Tuesday, September 12, 2023

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

The Role

We are seeking a skilled Machine Learning Systems Engineer to join a small, versatile team designing, implementing, and optimizing machine learning solutions at Ghost. Combining a deep understanding of both machine learning techniques and distributed systems engineering principles, the MLS Engineer will play a crucial role in accelerating machine learning research and productizing a scalable solution to self-driving.

What you will do:

  • Collaborate with cross-functional teams to define the architecture and build the infrastructure required for machine learning solutions
  • Integrate best practices from ongoing machine learning research into existing systems, ensuring smooth interoperability as researchers rapidly explore new ideas
  • Create data preprocessing pipelines to clean, transform, and/or augment data as necessary
  • Implement CI/CD pipelines for deploying machine learning models, including automated testing, validation, and deployment
  • Optimize and scale systems to accommodate growing data and user demands.
  • Monitor, analyze, and establish systems to track the health and performance of data flows and model training systems, considering latency, throughput, and resource constraints
  • Identify issues, diagnose root causes, and implement solutions to ensure uninterrupted service


  • BS/MS/PhD in Computer Science or related field
  • 8+ years of software development experience
  • 4+ years in a machine learning or systems software engineering role
  • Experience with all or subset of: 1) Cloud platforms (AWS, Azure, GCP), 2) Containerization technologies (Docker, Kubernetes), 3) Machine learning techniques, algorithms, and frameworks (PyTorch, TensorFlow, etc.)
  • Proficiency in programming languages such as Python, Scala, or C++
  • Strong problem-solving skills with a focus on practical, scalable solutions
  • Excellent communication skills and ability to collaborate across teams

Nice to haves:

  • Previous experience in deploying data pipelines and machine learning models in production environments


  • Medical, Vision and Dental coverage (PPO, HMO, and HSA options available; 100% premium coverage of several PPO and HMO plans for employees)
  • 401(k) plan
  • Life Insurance


Compensation for this role consists of a base salary and an options grant, with the base salary expected to range from $175,000 to $250,000+. Individual compensation will be commensurate with the candidate’s experience.

Ghost is committed to equal employment opportunity. We will not discriminate against employees or applicants for employment on any legally‑recognized basis [“protected class”] including, but not limited to: veteran status, uniform service member status, race, color, religion, sex, national origin, age, physical or mental disability or any other protected class under federal, state or local law.