Aerospace AI Research Engineer: LLMs & Multimodal

Archer; San Jose (CA); On-site; USD 240,000 - 252,000; Full time

Job summary

Archer is seeking an experienced AI/ML Engineer to design and deploy cutting-edge ML models, including LLMs and multimodal architectures, in production environments. You will work with research engineers to prototype from academic literature to scalable systems, emphasizing reproducibility and observability.

The role requires 3+ years (or 2+ with alternate PhD) of experience with Python, PyTorch, and large-scale data pipelines, plus strong collaboration with cross-functional teams.

Qualifications

• Design/implement novel ML and DL algorithms focusing on LLMs, ASR, and multimodal architectures.
• Experience deploying production ML systems with observability and retraining workflows.

Responsibilities

• Design, implement, and evaluate novel ML and DL algorithms for LLMs, ASR, and multimodal tasks.
• Iterate on AI model development from data to deployment for complex seq2seq tasks.
• Collaborate with research engineers to prototype solutions from literature.
• Conduct experiments to benchmark techniques and evaluate model behavior.
• Develop tools to support scalable, reproducible research and deployment.
• Present findings to leadership concisely and convincingly.
• Stay current with Generative AI and Speech/Language Models innovations.
• Assist in transitioning research prototypes into production-ready systems.

Skills

Python

PyTorch

NLP

Distributed processing

OpenCV

Education

Master’s degree (AI/CS/Computational Sciences/ML/Informatics)

PhD in AI/CS/Computational Sciences/ML/Informatics

Tools

Grafana

Prometheus

Statsig

Huggingface

NLTK

spaCy

Transformers models

Spark

Databricks

OpenCV

YOLO

Vision transformers

Job description

Archer is seeking an experienced AI/ML Engineer to design and deploy cutting-edge ML models, including LLMs and multimodal architectures, in production environments. You will work with research engineers to prototype from academic literature to scalable systems, emphasizing reproducibility and observability.

The role requires 3+ years (or 2+ with alternate PhD) of experience with Python, PyTorch, and large-scale data pipelines, plus strong collaboration with cross-functional teams.