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