{"id":1134780,"url":"https://alion.io/job/alldus-ml-ai-research-engineer","title":"ML / AI Research Engineer","company":{"id":1044164,"name":"Alldus","domain":"alldus.com","url":"https://alion.io/company/alldus","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"E","score":31,"open_postings":113,"ghost_share":0.814,"stale_share":0.027,"repost_share":0.956,"time_to_fill_p50_days":67,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":180000,"max":240000,"currency":"USD","period":"year","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AutoGPT","optional":false},{"name":"BabyAGI","optional":false},{"name":"DeepSpeed","optional":false},{"name":"DPO","optional":false},{"name":"FAISS","optional":false},{"name":"Falcon","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Function Calling","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Mistral","optional":false},{"name":"Mixtral","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"PPO","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reranking","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"SQL","optional":false},{"name":"TGI","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"PEFT","optional":true}],"status":"closed","first_seen_at":"2026-08-07T00:00:00Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-09T22:38:04Z","board_verified":false,"closed_at":"2026-11-09T22:38:04Z","days_open":94,"trust":{"level":"ghost","repost_count":5,"flags":["repost"],"days_open":94},"description":"Our client, a venture-backed AI Startup, is hiring a talented ML/AI Research Engineer to join their team in San Francisco. The successful candidate will lead the design, training, evaluation and optimization of agent-native AI systems, working at the cutting edge of LLMs, vector search, graph reasoning and reinforcement learning to build the intelligence layer on top of their enterprise data fabric.\nResponsibilities\nFine-tune and evaluate open-source LLMs (e.g. LLaMA 3, Mistral, Falcon, Mixtral) for enterprise-grade applications.\n\nBuild and optimize RAG pipelines using tools such as LangChain, LangGraph, LlamaIndex or Dust.\n\nDevelop and iterate on agent architectures (ReAct, AutoGPT, BabyAGI, OpenAgents) using real-world enterprise workflows.\n\nDesign embedding-based memory systems with efficient, high-performance retrieval strategies.\n\nImplement reinforcement learning pipelines (RLHF, DPO, PPO) to improve agent behavior and decision-making.\n\nCreate scalable evaluation frameworks, including synthetic evaluations, trace capture and explainability tooling.\n\nOwn model observability, drift detection and alignment strategies across production systems.\n\nOptimize inference latency and GPU utilization across cloud and on-premise infrastructure.\n\nSkillset\nStrong experience fine-tuning open-source LLMs using frameworks such as HuggingFace, DeepSpeed, vLLM, FSDP and LoRA/QLoRA.\n\nHands-on experience with modern alignment techniques, including SFT, RLHF and DPO pipelines.\n\nProven ability to build high-quality training datasets and robust evaluation frameworks for LLM systems.\n\nDeep understanding of scaling and optimization trade-offs, including batching, context windows, precision and quantization.\n\nExperience building and deploying production-grade RAG systems.\n\nFamiliarity with orchestration and retrieval tools such as LangChain, LangGraph and LlamaIndex, and vector databases (Weaviate, Qdrant, FAISS).\n\nExperience working across structured (SQL, graph) and unstructured data sources.\n\nExperience designing agent-based systems with memory, tool use, and multi-step reasoning.\n\nStrong understanding of agent workflows (e.g. Plan-Act-Reflect), including self-correction and multi-agent systems.\n\nExpertise in inference and retrieval optimization, including chunking strategies, reranking, and low-latency deployment (e.g. vLLM, TGI).\n\nBenefits\nSalary: $180k - 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