Senior Machine Learning Engineer (Inference Platform)

Role Overview

We are looking for a Senior Machine Learning Engineer to take ownership of the production inference systems that power a high-scale AI-driven conversational shopping experience. The position sits at the intersection of machine learning, distributed systems, and platform engineering, with a strong focus on performance, scalability, and cost efficiency.

What You Will Do

You will be responsible for building, operating, and optimizing the end-to-end ML serving infrastructure, ensuring models run reliably under real-world production load. You will collaborate closely with ML engineers, data teams, product, and DevOps to bring models from experimentation into production seamlessly.

Why It Might Be a Fit

This is a high-impact role where your architectural decisions directly shape user experience and system performance. You will work in a fast-paced, startup-like environment where ownership and technical depth are essential. The role offers significant autonomy in defining the future of the inference platform.

Requirements

• 5–8+ years of experience in ML engineering, software engineering, or platform/infrastructure roles with ownership of production ML systems
• Hands-on experience operating LLM serving frameworks such as vLLM, TGI, TensorRT-LLM, or SGLang in real production environments
• Strong Python skills and solid understanding of distributed systems and backend engineering principles
• Experience with cloud platforms (AWS, GCP, or Azure) and ML lifecycle tooling, including model registries and deployment systems
• Deep understanding of inference optimization concepts such as KV caching, batching strategies, GPU memory behavior, and latency bottlenecks
• Experience supporting heterogeneous ML workloads including LLMs, embeddings, and extraction models
• Strong ability to balance latency, throughput, reliability, and infrastructure cost trade-offs
• Experience working in fast-paced, high-growth environments with evolving technical requirements
• Excellent problem-solving, communication, and collaboration skills across technical and non-technical teams

Benefits

• Competitive compensation aligned with experience and impact
• Remote-first flexibility within the United States
• Opportunity to shape core AI infrastructure powering a large-scale consumer-facing product
• High ownership role with influence over architecture and technical direction
• Collaborative, cross-functional engineering environment
• Exposure to cutting-edge LLM and AI inference technologies
• Fast-paced startup culture with strong autonomy and technical depth