Software Engineer, ML Infrastructure

Role Overview

We are hiring a Founding ML Infrastructure Engineer to own the end-to-end deployment, optimization, and operation of our suits of models in production.

This is a core founding role focused on building and operating production-grade LLM systems
. You will apply deep knowledge of model internals to deploy, optimize, and run modern LLMs at scale
, owning performance end-to-end across latency, throughput, and reliability
.

You will design and operate the full ML serving stack from model artifacts to GPU execution, and work closely with Product and ML teams to ensure our models can support high QPS, strict SLAs, and production correctness
.

This role is ideal for someone who deeply understands how LLMs work internally but chooses to specialize in making them fast, stable, and production-ready
.

Realm Labs is an AI trust and security startup. We help enterprises detect, debug, and prevent AI’s misbehaviors in production. We are backed by top VCs and serve some of the most iconic global enterprises.

• 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 Tensor Flow 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

• 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)
  • Tensor Flow (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
  .

• 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.

• 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.

• 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.