Software Engineer, ML Infrastructure

Role Overview

About Realm Labs

Key Responsibilities

• Own the end-to-end LLM inference stack, including:
  • Model loading and execution
  • GPU utilization and memory efficiency
  • Runtime performance tuning
  • Production deployment and scaling
• Design and operate high-performance LLM serving systems using technologies such as:
  • vLLM, TensorRT / TensorRT-LLM, Triton Inference Server, SGLang
• Optimize inference across:
  • Latency
  • Throughput (QPS)
  • GPU memory footprint
  • Cost efficiency
• Work hands-on with PyTorch and TensorFlow models, including:
  • Model graph understanding
  • Attention mechanisms, KV cache behavior, batching strategies
  • Precision tradeoffs (FP16, BF16, INT8, etc.)
• Build and maintain production-grade GPU services:
  • Multi-model serving
  • Autoscaling strategies
  • Fault isolation and graceful degradation
• Collaborate with application and platform teams to:
  • Define serving APIs
  • Ensure correctness and safety of outputs
  • Debug production issues end-to-end
• Build a reproducible model training and versioning system for customer deployments
• Establish best practices for:
  • Model versioning
  • Rollouts and rollbacks
  • Performance benchmarking
  • Production validation

Expected Qualifications

• 5+ years of professional experience in ML infrastructure, systems engineering, or production ML roles.
• Strong software engineering fundamentals; ability to write robust, maintainable production code.
• Deep hands-on experience with LLM inference infrastructure, including:
  • PyTorch (required)
  • TensorFlow (working knowledge)
• Proven experience with GPU inference optimization, including:
  • TensorRT / TensorRT-LLM
  • vLLM
  • Triton Inference Server
  • SGLang or similar serving runtimes
• Strong understanding of LLM internals, such as:
  • Transformer architectures
  • Attention and KV caching
  • Batching, streaming, and token-level generation
• Experience running ML systems in production with high traffic and SLAs
• Comfortable working in Linux-based, cloud production environments

Preferred Qualifications

• Experience deploying LLMs on Kubernetes and GPU clusters.
• Familiarity with CUDA, NCCL, or low-level GPU performance concepts.
• Experience with:
  • Model sharding and parallelism strategies
  • Multi-GPU inference
  • Streaming inference systems
• Knowledge of observability for ML systems (metrics, latency breakdowns, GPU monitoring).
• Experience working at startups or owning systems with minimal abstraction layers.

Additional Information

• This is a founding, high-ownership role with direct impact on core product capabilities.
• You will be expected to build, run, and own systems end-to-end.
• The role may include limited on-call responsibilities aligned with production ownership.

Compensation & Benefits

• Market aligned compensation and benefits
• Founding engineer equity (Equity is a significant component of this role and will be discussed)
• Medical, Dental, Vision, Life insurance, 401-K, In-office lunch etc.