AI Systems Engineer

Key Skills

Aartificial intelligence, C++, Deep Learning, Large Language Models - LLMs, Python Programming, PyTorch, TensorFlow

Job Description

We Are Hiring: Machine Learning Engineer – Deployment & Inference

You will help build large-scale deployment of machine learning models for both research and inference.

Our Current Stack: Python, C++, TensorRT-LLM, Kubernetes.

Responsibilities

• Develop APIs for AI inference used by both internal and external customers.
• Set up infrastructure and ensure efficient, reliable deployment.
• Benchmark performance and reduce bottlenecks across the inference stack.
• Enhance system reliability, observability, and respond to outages.

Qualifications

• Experience with ML systems and deep learning frameworks (e.g., PyTorch, TensorFlow, ONNX).
• High-level familiarity with LLM architecture, including key components (Multi-Head, Multi/Grouped-Query, and common layers).
• Experience deploying reliable, distributed, real-time systems at scale.

Compensation & Benefits

Salary: $190,000 - $250,000 (final offer depends on experience and expertise).

Equity: Equity is included as part of the total compensation package.

Benefits: Comprehensive health, dental, and vision insurance for you and your dependents. Includes a 401(k) plan.

About Perplexity

Since launching the world’s first fully functional conversational answer engine in 2022, Perplexity has experienced tremendous growth. In 2024 alone, our daily queries increased from 2.5 million to around 20 million by December. We also offer Perplexity Enterprise Pro, serving leading companies such as Nvidia, the Cleveland Cavaliers, Bridgewater, and Zoom.

To support our rapid expansion, we’ve raised substantial funding from top-tier investors, including IVP, NEA, Jeff Bezos, NVIDIA, Databricks, Bessemer Venture Partners, Elad Gil, Nat Friedman, Daniel Gross, Naval Ravikant, Tobi Lutke, and other industry leaders. Our team grew nearly 300% in 2024, and we’re just getting started.

Join us as we redefine search and knowledge discovery.