Machine Learning Systems (MLS) Engineer
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.
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.