Staff+ Software Engineer, ML Inference Path

The shape of it

In the posting’s own words

We’re growing the team and looking for engineers who have deep expertise in productionizing ML systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale. And your tooling and infrastructure will be used for every model launch, which are becoming more complex, and more frequent.

What it asks for · 7

• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have built automated or self-service deployment pipelines and eval infrastructure allowing researchers to roll out classifiers and models independently
• Have implemented A/B testing frameworks and experimentation infrastructure for ML systems
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Also a plus

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Working with large language models and modern transformer architectures
• Developing monitoring and alerting systems for ML model performance and data drift
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements

What the job covers

• Design and build scalable ML infrastructure to support real-time safety deployments across our classifier and model ecosystem
• Build monitoring and observability tools to track classifier performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

Tools and skills named

Words the posting leans on

• safety13×
• systems12×
• infrastructure7×
• model7×
• production7×
• classifiers5×
• research5×
• build4×
• deployment4×
• experience4×
• claude3×
• every3×
• frameworks3×
• inference3×
• path3×
• researchers3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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