Ml Infrastructure Engineer (AI)

TL;DR

Ml Infrastructure Engineer (AI): Build and scale critical infrastructure powering AI safety systems with an accent on real-time and batch classifier evaluations, monitoring, and reliability. Focus on designing scalable ML platforms, optimizing inference latency, and productionizing safety research into robust systems.

Location: Hybrid with at least 25% office presence in San Francisco, USA

Salary: $320,000 - $405,000 USD annually

Company

hirify.global is a public benefit corporation focused on creating reliable, interpretable, and steerable AI systems that are safe and beneficial for society.

What you will do

• Design and build scalable ML infrastructure for real-time and batch safety evaluations
• Develop monitoring and observability tools for model performance and system health
• Collaborate with research teams to productionize safety research
• Optimize inference latency and throughput while ensuring high reliability
• Implement automated testing, deployment, and rollback systems for ML models
• Partner with Safeguards, Security, and Alignment teams to meet safety and production needs

Requirements

• Must have 5+ years experience building production ML infrastructure in safety-critical domains
• Proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX
• Experience with cloud platforms (AWS, GCP) and Kubernetes
• Knowledge of distributed systems and building high-throughput, low-latency systems
• Experience with data engineering tools such as Spark and Airflow
• Bachelor's degree or equivalent experience required

Nice to have

• Experience with large language models and transformer architectures
• Implementing A/B testing and experimentation infrastructure
• Developing monitoring and alerting for ML model performance and data drift
• Building automated labeling and human-in-the-loop workflows
• Knowledge of privacy-preserving ML techniques and compliance

Culture & Benefits

• Competitive compensation including equity and benefits
• Visa sponsorship available with immigration support
• Flexible working hours and hybrid work policy
• Generous vacation and parental leave
• Collaborative and impact-driven research environment

Hiring process

• Rolling application review
• Interviews assessing technical skills and collaboration
• Evaluation of fit with AI safety mission and team culture