{"id":624361,"url":"https://alion.io/job/fieldai-agentic-aiml-engineer-multimodal","title":"Agentic AI/ML Engineer, Multimodal","company":{"id":672883,"name":"FieldAI","domain":"fieldai.com","url":"https://alion.io/company/fieldai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":82,"open_postings":103,"ghost_share":0.223,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":62,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Irvine, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":117000,"max_usd":263000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":398},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"DeepSpeed","optional":false},{"name":"FAISS","optional":false},{"name":"Few-Shot Learning","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Function Calling","optional":false},{"name":"Hugging Face","optional":false},{"name":"Interpretability","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LoRA","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"vLLM","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2025-09-26T23:52:38Z","employer_posted_date":"2025-09-26","last_verified_at":"2026-11-01T19:04:42Z","board_verified":true,"closed_at":null,"days_open":400,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":400},"description":"About the Job\nOur Field Foundation Model (FFM) powers a global fleet of autonomous robots that capture massive streams of multimodal data across diverse, dynamic environments every day. As part of the Insight Team our mission is to transform this raw, multimodal data into actionable insights that empower our customers and engineers to deliver value. Field-insight Foundation Model (FiFM) is at the core of how we transform multimodal data from autonomous robots into actionable insights. As an AI/ML Engineer on the FiFM team, you will drive research and model development for one of Field AI’s most ambitious initiatives. Your work will span computer vision, vision-language models (VLMs), multimodal scene understanding, and long-memory video analysis and search, with a strong emphasis on agentic AI (tool use, memory, multimodal retrieval-augmented generation).This is a full-cycle ML role: you’ll curate datasets, fine-tune and evaluate models, optimize inference, and deploy them into production. It’s a blend of applied research and engineering, requiring creativity, rapid experimentation, and rigorous problem-solving. While FiFM is your primary focus, you’ll also contribute to broader perception and insight-generation initiatives across Field AI.\nWhat You’ll Get To Do\nTrain and fine-tune million- to billion-parameter multimodal models, with a focus on computer vision, video understanding, and vision-language integration.\nTrack state-of-the-art research, adapt novel algorithms, and integrate them into FiFM.\nCurate datasets and develop tools to improve model interpretability.\nBuild scalable evaluation pipelines for vision and multimodal models.\nContribute to model observability, drift detection, and error classification.\nFine-tune and optimize open-source VLMs and multimodal embedding models for efficiency and robustness.\nBuild and optimize Multi-VectorRAG pipelines with vector DBs and knowledge graphs.\nCreate embedding-based memory and retrieval chains with token-efficient chunking strategies.\nWhat You Have\nMaster’s/Ph.D. in Computer Science, AI/ML, Robotics, or equivalent industry experience.\n2+ years of industry experience or relevant publications in CV/ML/AI.\nStrong expertise in computer vision, video understanding, temporal modeling, and VLMs.\nProficiency in Python and PyTorch with production-level coding skills.\nExperience building pipelines for large-scale video/image datasets.\nFamiliarity with AWS or other cloud platforms for ML training and deployment.\nUnderstanding of MLOps best practices (CI/CD, experiment tracking).\nHands-on experience fine-tuning open-source multimodal models using HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA.\nKnowledge of precision tradeoffs (FP16, bfloat16, quantization) and multi-GPU optimization.\nAbility to design scalable evaluation pipelines for vision/VLMs and agent performance.\nThe Extras That Set You Apart\nExperience with Agentic/RAG pipelines and knowledge graphs (LangChain, LangGraph, LlamaIndex, OpenSearch, FAISS, Pinecone).\nFamiliarity with agent operations logging and evaluation frameworks.\nBackground in 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