Ml Infrastructure Engineer (AI)
ΠΡΡΡ & Π‘ΠΎΠΏΡΠΎΠ²ΠΎΠ΄
ΠΠ»Ρ ΠΌΡΡΡΠ° Ρ ΡΡΠΎΠΉ Π²Π°ΠΊΠ°Π½ΡΠΈΠ΅ΠΉ Π½ΡΠΆΠ΅Π½ Plus
ΠΠΏΠΈΡΠ°Π½ΠΈΠ΅ Π²Π°ΠΊΠ°Π½ΡΠΈΠΈ
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
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
ΠΡΠ΄ΡΡΠ΅ ΠΎΡΡΠΎΡΠΎΠΆΠ½Ρ: Π΅ΡΠ»ΠΈ ΡΠ°Π±ΠΎΡΠΎΠ΄Π°ΡΠ΅Π»Ρ ΠΏΡΠΎΡΠΈΡ Π²ΠΎΠΉΡΠΈ Π² ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ iCloud/Google, ΠΏΡΠΈΡΠ»Π°ΡΡ ΠΊΠΎΠ΄/ΠΏΠ°ΡΠΎΠ»Ρ, Π·Π°ΠΏΡΡΡΠΈΡΡ ΠΊΠΎΠ΄/ΠΠ, Π½Π΅ Π΄Π΅Π»Π°ΠΉΡΠ΅ ΡΡΠΎΠ³ΠΎ - ΡΡΠΎ ΠΌΠΎΡΠ΅Π½Π½ΠΈΠΊΠΈ. ΠΠ±ΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎ ΠΆΠΌΠΈΡΠ΅ "ΠΠΎΠΆΠ°Π»ΠΎΠ²Π°ΡΡΡΡ" ΠΈΠ»ΠΈ ΠΏΠΈΡΠΈΡΠ΅ Π² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ. ΠΠΎΠ΄ΡΠΎΠ±Π½Π΅Π΅ Π² Π³Π°ΠΉΠ΄Π΅ β