1.68 Agentic AI/ML Engineer - Multimodal
Team: Area 1: ML, AI, Autonomy
Location: Irvine, CA
Commitment: Full time
Workplace Type: onsite
What You’ll Get To Do:
- Train and fine-tune million- to billion-parameter multimodal models, with a focus on computer vision, video understanding, and vision-language integration.
- Track state-of-the-art research, adapt novel algorithms, and integrate them into FiFM.
- Curate datasets and develop tools to improve model interpretability.
- Build scalable evaluation pipelines for vision and multimodal models.
- Contribute to model observability, drift detection, and error classification.
- Fine-tune and optimize open-source VLMs and multimodal embedding models for efficiency and robustness.
- Build and optimize Multi-VectorRAG pipelines with vector DBs and knowledge graphs.
- Create embedding-based memory and retrieval chains with token-efficient chunking strategies.
What You Have:
- Master’s/Ph.D. in Computer Science, AI/ML, Robotics, or equivalent industry experience.
- 2+ years of industry experience or relevant publications in CV/ML/AI.
- Strong expertise in computer vision, video understanding, temporal modeling, and VLMs.
- Proficiency in Python and PyTorch with production-level coding skills.
- Experience building pipelines for large-scale video/image datasets.
- Familiarity with AWS or other cloud platforms for ML training and deployment.
- Understanding of MLOps best practices (CI/CD, experiment tracking).
- Hands-on experience fine-tuning open-source multimodal models using HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA.
- Knowledge of precision tradeoffs (FP16, bfloat16, quantization) and multi-GPU optimization.
- Ability to design scalable evaluation pipelines for vision/VLMs and agent performance.
The Extras That Set You Apart:
- Experience with Agentic/RAG pipelines and knowledge graphs (LangChain, LangGraph, LlamaIndex, OpenSearch, FAISS, Pinecone).
- Familiarity with agent operations logging and evaluation frameworks.
- Background in optimization: token cost reduction, chunking strategies, reranking, and retrieval latency tuning.
- Experience deploying models under quantized (int4/int8) and distributed multi-GPU inference.
- Exposure to open-vocabulary detection, zero/few-shot learning, multimodal RAG.
- Knowledge of temporal-spatial modeling (event/scene graphs).
- Experience deploying AI in edge or resource-constrained environments.
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