Machine Learning Engineer, Ops

A bit about Cantina:

Cantina, founded by Sean Parker, is a new social platform with the most advanced AI character creator. Build, share, and interact with AI bots and your friends directly in the Cantina or across the internet.

Cantina bots are lifelike, social creatures, capable of interacting wherever humans go on the internet. Recreate yourself using powerful AI, imagine someone new, or choose from thousands of existing characters. Bots are a new media type that offer a way for creators to share infinitely scalable and personalized content experiences combined with seamless group chat across voice, video, and text.

If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future!

A bit about the role: 

You will design, implement, and maintain robust infrastructure to support training, deployment, and monitoring of ML models at scale and will collaborate with research scientists to integrate ML models into production systems and applications. You will also optimize performance and reliability of ML infrastructure, including distributed computing frameworks, container orchestration, and cloud services and will develop tools and automation to streamline the ML development lifecycle, including data preprocessing, feature engineering, model training, and evaluation.

As a ML Engineer, you will:

• Propose, design, and prototype scalable and reliable systems to handle model inference at scale

• Develop and maintain automated pipelines & infrastructure for model training, testing, and deployment

• Improve CI/CD practices to enable rapid and reliable model updates, versioning to speed up iterative learning and model improvements.

• Respond to alerts and troubleshoot issues related to model deployment and infrastructure

• Design and develop robust endpoint APIs to enable software engineers across the product to integrate ML APIs into applications

• Develop, deploy, maintain and extend new technologies to enable and enhance data scientists' capabilities for every step of the ML model life cycle
  

A bit about you:

• Expertise building ML systems from scratch.

• 5+ years of experience in software engineering, ML engineering, or related roles.

• Strong background in building and maintaining infrastructure for ML and data science projects for distributed computing systems

• Proficiency in programming languages, notably Rust and C++, and experience with relevant libraries and frameworks.

• Hands-on experience with cloud platforms such as AWS, GCP, or Azure, and familiarity with containerization technologies (e.g., Docker, Kubernetes).

• Solid understanding of distributed systems, networking, data streaming and storage technologies.

Location:

We have offices located in Sunnyvale, CA, San Francisco, CA and Brooklyn, NY. While we have a strong focus on individuals near our office hubs, we offer fully remote and hybrid employment opportunities.

Pay Equity: 

In compliance with Pay Transparency Laws, the base salary range for this role is between $250,000 - $350,000 for those located in the San Francisco Bay Area, New York City and Seattle, WA. When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data.

Application Process: Please submit your resume, cover letter, and any relevant portfolio or publications demonstrating your research contributions in AI.

Benefits Summary:

• Health Care —  99% of premiums for medical, vision, dental are fully paid for by Cantina, plus One Medical membership.

• Monthly Stipend — $500/month to use on whatever you’d like! 

• Rest and Recharge — 15 PTO days per year, 9 sick days, 13 paid company holidays, and offices closed for winter break (Christmas Eve to New Years Day)! 

• 401(K) — Eligible to participate on day one of employment.

• Parental Leave & Fertility Support 

• Competitive Salary & Equity 

• Lunch and snacks provided for in-office employees. 

• WFH equipment provided for full-time hybrid/remote employees.