Senior AI Systems Engineer
Role details
Job location
Tech stack
Job description
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.
- Infrastructure & Orchestration: Deploy, scale, and manage resilient infrastructure services tailored for distributed AI model training and low-latency inference.
- Maturity & MLOps: Utilize and maintain end-to-end tooling-including MLflow for experiment tracking and model registry-to streamline and optimize the AI development lifecycle.
- Compute & Inference Optimization: Leverage specialized frameworks to maximize hardware utilization, managing multi-cloud compute scheduling alongside advanced LLM serving engines.
- Cross-Functional Collaboration: 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.
Requirements
Do you have experience in Systems engineering?, Do you have a Bachelor's degree?, * Education: BS/MS/PhD degree in Computer Science, Software Engineering, or a related field.
- Experience: 3+ years of professional software engineering experience with a dedicated focus on AI/ML systems, high-performance computing (HPC), or ML infrastructure.
- Multi-Cloud & Compute Management: Familiarity with hyper-scaler infrastructure (AWS) alongside specialized AI-centric bare-metal and GPU clouds (Nebius AI Cloud).
- Cloud-Native Orchestration & Abstraction: 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.
- LLM Serving Optimization: Deep architectural understanding of large language models and the system infrastructure required to serve them at scale using frameworks like vLLM and SGLang.
- Data Engineering for AI: 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)., * 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).
Benefits & conditions
At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. We drive a pay-for-performance culture and reward performance that supports the Company's business strategy. For this position we are targeting a base pay between $160,000.00 - $180,000.00 Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience.