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Ml Infrastructure Engineer (AI)

320 000 - 405 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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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

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