Machine Learning Engineer, AI Inference Solutions – Early Career

• Contribute production code across the ML deployment platform, model-optimization workflows, and inference benchmarking/profiling infrastructure
• Pair with senior engineers on deployment workflows, performance investigations, model-optimization experiments, and platform tooling
• Build, test, and maintain platform tools such as validators, performance probes, parity and sensitivity analyzers, and agentic specialists
• Investigate and help root-cause production deployment or performance issues involving compiler, kernel, runtime, and parity bugs
• Collaborate with kernels, compiler, reduced-precision, parity, and model-development teams to plan and execute model deployments to the AV stack
• Participate in code reviews, design discussions, and technical documentation
• Learn and follow secure coding, safety, and compliance practices for on-vehicle autonomous-driving software

• Recently completed or completing a Bachelor’s or Master’s degree by Spring 2026 in Computer Science, ECE, or a related technical field; degree must be completed before start date
• Strong computer science fundamentals in data structures, algorithms, operating systems, and computer architecture
• Solid coding skills in Python and/or C++, demonstrated through coursework, internships, or substantial projects
• Hands-on AI/ML experience through classes, research, internships, or personal projects
• Depth in at least one of computer architecture, operating systems, distributed systems, or compilers
• Demonstrated software-engineering experience through internships, coursework, open-source, research code, or competitions
• Experience with—or strong interest in—coding assistants/agents such as Cursor, Claude Code, or Git Hub Copilot
• Ability to work effectively in collaborative, cross-functional teams and communicate clearly in writing and verbally
• Preferred: internship, research, or advanced coursework in ML systems, ML compilers, GPU programming, inference optimization, or distributed training/serving infrastructure
• Preferred: familiarity with PyTorch and modern ML compiler/runtime stacks such as torch.compile, TensorRT, ONNX, Triton Inference Server, or vLLM
• Preferred: exposure to model optimization or GPU profiling tools
• Preferred: familiarity with Airflow, Temporal, Flyte, Ray, or Kubeflow
• Preferred: experience building agentic or LLM-powered tools or workflows
• Preferred: open-source contributions related to PyTorch, TensorRT, vLLM, OpenAI Triton, or similar projects
• Preferred: coursework, projects, or publications touching ML systems
• Preferred: familiarity with C++ and Linux development

Demonstrates strong coding skills in Python and C++, with hands-on experience in AI/ML and a solid understanding of computer science fundamentals. Capable of collaborating effectively in cross-functional teams while adhering to secure coding and compliance practices for autonomous-driving software.

• Python Programming
• C++ Programming
• AI/ML Experience
• Model Optimization
• Collaboration Skills

• Data Structures
• Algorithms
• Operating Systems
• Computer Architecture
• Software Engineering
• Distributed Systems
• Compilers
• Performance Optimization
• GPU Programming
• Inference Optimization

• Effective Communication
• Team Collaboration

• ML Deployment
• Model Development
• Performance Benchmarking
• Secure Coding Practices
• Autonomous Driving Software

• Py Torch
• TensorRT
• ONNX
• Triton Inference Server
• Cursor
• Claude Code
• Git Hub Copilot
• Airflow
• Temporal
• Kubeflow