Software Engineering – Inference Engineer

Location: San Francisco, CA (Onsite | Remote)

About Virtue AI

Virtue AI sets the standard for advanced AI security platforms. Built on decades of foundational and award-winning research in AI security, its AI-native architecture unifies automated red-teaming, real-time multimodal guardrails, and systematic governance for enterprise apps and agents. Deploy in minutes—across any environment—to keep your AI protected and compliant. We are a well-funded, early-stage startup founded by industry veterans, and we're looking for passionate builders to join our core team.

What You’ll Do

As an Inference Engineer, you will own how models are served in production. Your job is to make inferences fast, stable, observable, and cost-efficient—even under unpredictable workloads.

You will:

• Serve and optimize LLM, embedding, and other ML models' inference across multiple model families

• Design and operate inference APIs with clear contracts, versioning, and backward compatibility

• Build routing and load-balancing logic for inference traffic

• Multi-model routing

• Fallback and degradation strategies

• vLLM or SGLang

• Package inference services into production-ready Docker images

• Implement logging and metrics for inference systems

• Latency, throughput, token counts, GPU utilization

• Prometheus-based metrics

• Analyze server uptime and failure modes

• GPU OOMs, hangs, slowdowns, fragmentation

• Recovery and restart strategies

• Design GPU and model placement strategies

• Model sharding, replication, and batching

• Tradeoffs between latency, cost, and availability

• Work closely with backend, platform (Cloud, DevOps), and ML teams to align inference behavior with product requirements

What Makes You a Great Fit

You understand that inference is a systems problem, not just a model problem. You think in QPS, p99 latency, GPU memory, and failure domains.

Required Qualifications

• Bachelor’s degree or higher in CS, CE, or related field

• Strong experience serving LLMs and embedding models in production

• Hands-on experience designing:

• Inference APIs

• Load balancing and routing logic

• Experience with SGLang, vLLM, TensorRT, or similar inference frameworks

• Strong understanding of GPU behavior

• Memory limits, batching, fragmentation, utilization

• Experience with:

• Docker

• Prometheus metrics

• Structured logging

• Ability to debug and fix real inference failures in production

• Experience with autoscaling inference services

• Familiarity with Kubernetes GPU scheduling

• Experience supporting production systems with real SLAs

• Proven ability to debug and fix inference failures in production

• Comfortable operating in a fast-paced startup environment with high ownership

Preferred Qualifications

• Experience with GPU-level optimization

• Memory planning and reuse

• Kernel launch efficiency

• Reducing fragmentation and allocator overhead

• Experience with kernel- or runtime-level optimization

• CUDA kernels, Triton kernels, or custom ops

• Experience with model-level inference optimization

• Quantization (FP8 / INT8 / BF16)

• KV-cache optimization

• Speculative decoding or batching strategies

• Experience pushing inference efficiency boundaries (latency, throughput, or cost)

Why Join Virtue AI

• Competitive salary + equity

• Direct ownership of inference reliability and performance

• Hard problems at the intersection of systems, GPUs, and AI

• Production impact – Your work directly affects latency, cost, and uptime

• Strong technical culture – Engineers who debug and optimize, not just prototype