Machine Learning Systems Engineer - Infrastructure & Runtime, Horizons

Key Skills

Aartificial intelligence, C++, Effective communication skills, Infrastructure as code, Kubernetes-K8s, Large Language Models - LLMs, Machine learning techniques, Terraform

Job Description

As an Infrastructure & Runtime Engineer on the Horizons team, you will build and maintain the foundational systems that enable our AI research. You'll work closely with researchers and engineers to develop robust infrastructure for large language model training, focusing on code execution environments, data pipelines, and performance optimization. Your work will directly support advances in reinforcement learning, agentic AI capabilities, and secure model evaluation systems.

Representative projects:

• Design and implement high-performance data pipelines for processing large-scale code datasets with an emphasis on reliability and reproducibility
• Build and maintain secure sandboxed execution environments using virtualization technologies like GVisor and Firecracker
• Develop infrastructure for reinforcement learning training environments, balancing security requirements with performance needs
• Optimize resource utilization across our distributed computing infrastructure through profiling, benchmarking, and systems-level improvements
• Collaborate with researchers to translate their requirements into scalable, production-grade systems for AI experimentation

You may be a good fit if you:

• Are proficient in Python and async/concurrent programming with frameworks like Trio
• Have experience with container technologies and virtualization systems
• Possess strong systems programming skills and understand performance optimization
• Enjoy solving complex infrastructure challenges at scale
• Have experience with data pipeline development and ETL processes
• Care deeply about code quality, testing, and performance
• Communicate effectively with both technical and research-focused team members
• Are passionate about developing safe and beneficial AI systems

Strong candidates may have:

• Experience with cloud infrastructure and Kubernetes orchestration
• Familiarity with infrastructure-as-code tools (Terraform, Pulumi, etc.)
• Experience contributing to open-source projects in systems or infrastructure
• Knowledge of Rust and/or C++ for performance-critical components
• Experience implementing security controls for code execution
• Comfort engaging with ML research concepts and translating them to engineering requirements

Strong candidates need not have:

• Formal certifications or education credentials
• Experience with LLMs, reinforcement learning, or machine learning research before

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The expected salary range for this position is:

Annual Salary:

$300,000 - $405,000 USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.