AI Workloads Engineer - Adaptiq

This is an experienced, hands-on software engineering position in a small AI workloads team. You will build Python-based inference and performance-modeling infrastructure, run and adapt real workloads, and turn measurements into findings that hardware, software and business stakeholders can use. The scope includes both existing and next-generation hardware. You will have room to take work through implementation, profiling and reporting rather than handling only narrowly defined tasks. Prior chip expertise is not

Responsibilities

Build and maintain features for inference workflows and performance models, contributing code through pull requests.

Model end-to-end inference behavior across simulation and execution on hardware.

Run and adapt open-source AI models to investigate how they perform on current or planned chips.

Instrument and profile workloads; analyze latency, throughput, batching and request-level statistics.

Investigate workflows involving assisted decoding, dynamic batching and key-value cache use.

Use SDK and framework-integration tools to identify full-stack bottlenecks.

Connect tools that do not already work together, and prototype runtime components or kernels when a workflow requires them.

Work with compiler, runtime and hardware design teams on potential improvements.

Experience

At least 4 years of software engineering experience, with substantial hands-on development.

Strong, current Python skills and the ability to understand and review the code you work with.

Strong PyTorch development experience.

Practical experience with LLMs or relevant AI systems beyond calling a hosted API, such as examining or adapting open-source models.

Understanding of transformer-based models and attention, with familiarity with inference concepts such as KV caches, batching or assisted decoding.

Sound problem-solving skills and the ability to explain your own work, decisions and results to different audiences.