What You'll DoAs 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.
What You Need- 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).
Bonus Qualifications- 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).
Please note that this job description is intended to provide a general overview of the position and does not include an exhaustive list of responsibilities and qualifications
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.
