Senior AI Systems Engineer

Archer•San Jose, CA

2d•$160,000 - $180,000•Onsite

About The Position

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise. Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members. As a Senior AI Systems Engineer, you will architect, deploy, and manage the critical infrastructure services required for large-scale AI model training and inference. You will ensure our machine learning platforms are robust and efficient, bridging the gap between raw data and high-performance AI models.

Requirements

BS/MS/PhD degree in Computer Science, Software Engineering, or a related field.
3+ years of professional software engineering experience with a dedicated focus on AI/ML systems, high-performance computing (HPC), or ML infrastructure.
Familiarity with hyper-scaler infrastructure (AWS) alongside specialized AI-centric bare-metal and GPU clouds (Nebius AI Cloud).
Hands-on experience with containerization (Docker) and production-grade orchestration (Kubernetes), paired with cloud-agnostic cluster abstractors like SkyPilot to manage multi-region GPU availability.
Deep architectural understanding of large language models and the system infrastructure required to serve them at scale using frameworks like vLLM and SGLang.
Experience building high-throughput data pipelines to support large-scale training, including proficiency in SQL, NoSQL, and columnar storage formats optimized for ML (e.g., Parquet).

Nice To Haves

Familiarity with audio processing, speech-to-text frameworks, or Automatic Speech Recognition (ASR) pipelines.
Prior experience or a deep technical interest in aerospace, aviation, or autonomous systems (e.g., safety-critical software, edge-AI deployments).

Responsibilities

Deploy, scale, and manage resilient infrastructure services tailored for distributed AI model training and low-latency inference.
Utilize and maintain end-to-end tooling—including MLflow for experiment tracking and model registry—to streamline and optimize the AI development lifecycle.
Leverage specialized frameworks to maximize hardware utilization, managing multi-cloud compute scheduling alongside advanced LLM serving engines.
Partner closely with AI researchers and Software Engineers to productionize cutting-edge models, establish monitoring systems, and debug complex performance bottlenecks at the hardware-software interface.