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We are seeking an exceptional Senior Machine Learning Engineer with deep expertise in Large Language Model systems and applied research in educational AI. The successful candidate will be responsible for designing and implementing enterprise-scale LLM infrastructure, contributing to research initiatives, and building systems that advance educational technology.

Project Overview

You will be designing and implementing next-generation LLM systems that serve thousands of users across research institutions and educational organizations globally. This includes building distributed ML platforms, implementing research innovations, and creating LLM applications that enhance educational experiences.

Responsibilities

1. LLM Architecture & Engineering: Design and implement large-scale LLM systems, establish technical standards for model deployment, and drive strategic technical decisions for ML infrastructure.
2. Platform Engineering: Build foundational ML platforms that enable rapid experimentation, deployment, and scaling of LLM applications. Design systems for multi-tenancy, global distribution, and enterprise-grade reliability.
3. Advanced MLOps & Infrastructure: Develop sophisticated ML infrastructure including automated training pipelines, multi-model serving architectures, advanced monitoring systems, and cost optimization strategies for large-scale deployments.
4. Research & Innovation: Execute research roadmaps in educational AI, collaborate with academic institutions, and translate cutting-edge research into production systems. Contribute to publications and patent applications.
5. Performance & Scale Engineering: Build systems capable of serving models at massive scale (10M+ requests/day), implement advanced optimization techniques, and design fault-tolerant distributed systems with sub-100ms latency requirements.
6. Educational AI Development: Create LLM-powered applications specifically designed for educational use cases, implement learning analytics, and build systems that enhance teaching and learning experiences.

Required Qualifications

Educational Background

Master’s degree in Computer Science, Machine Learning, or related field, or Bachelor’s degree with equivalent experience. PhD preferred for research contributions.

Professional Experience

• 5+ years of experience in Machine Learning Engineering with focus on production systems
• 2+ years of hands-on experience building and scaling LLM systems in production environments
• Experience building distributed systems serving millions of users

Advanced Engineering Skills

• Systems Programming: Expert-level proficiency in Python, with experience in Rust or C++
• ML Infrastructure: Extensive experience with ML platforms, model serving architectures, and distributed training systems (Ray, Horovod, DeepSpeed)
• Cloud Architecture: Deep expertise in cloud-native architectures and multi-region deployments
• Performance Engineering: Proven track record optimizing large-scale systems for latency and cost efficiency

LLM & Research Expertise

• Advanced LLM Technologies: Expert-level experience with model optimization, quantization, and efficient inference techniques
• Distributed Inference: Hands-on experience with model parallelism, pipeline parallelism, and distributed serving architectures
• Research Integration: Experience implementing research papers into production systems and contributing to open-source ML projects

MLOps & Platform Skills

• Platform Design: Experience building ML experimentation frameworks, model registries, and deployment automation
• Advanced Monitoring: Expertise in ML observability systems and automated performance monitoring
• Cost Optimization: Experience optimizing infrastructure costs for large-scale ML workloads

Preferred Qualifications

1. Research Background: PhD in ML/AI with publications in top-tier venues (NeurIPS, ICML, ICLR, ACL), or equivalent industry research experience
2. Enterprise Scale: Experience serving models at massive scale (>10M requests/day) with enterprise SLA requirements
3. Specialized Hardware: Deep expertise in GPU optimization, custom hardware acceleration (TPUs, FPGAs), and emerging ML accelerators
4. Open Source Contributions: Significant contributions to major ML frameworks or maintainer status in relevant repositories
5. Educational AI Expertise: Domain knowledge in educational technology, learning sciences, or academic research environments

What You’ll Build

• Advanced LLM Systems: Design and implement cutting-edge LLM serving infrastructure with enterprise-grade reliability and performance
• Research Infrastructure: Develop platforms enabling rapid experimentation and seamless research-to-production workflows
• Educational AI Applications: Build innovative LLM-powered tools that transform teaching and learning experiences
• Performance Innovation: Create novel optimization techniques and serving architectures that push the boundaries of LLM efficiency

Technical Environment

Core Technology Stack

• Languages: Python, Rust, C++, CUDA, SQL
• ML Stack: PyTorch, JAX, Triton, Custom CUDA kernels, Distributed training frameworks
• Infrastructure: Kubernetes, Terraform, Multi-cloud architectures
• Data Systems: Distributed databases, Real-time streaming (Kafka), Vector databases
• Monitoring: ML observability platforms, Custom metrics systems