{"id":85929,"url":"https://alion.io/job/tensorlake-senior-applied-research-scientist-core-ai-document-intelligence","title":"Senior Applied Research Scientist – Core AI & Document Intelligence","company":{"id":34138,"name":"Tensorlake","domain":"tensorlake.ai","url":"https://alion.io/company/tensorlake","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":139000,"max_usd":264000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":370},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Computer Vision","optional":false},{"name":"Context Engineering","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-01-08T19:13:49Z","employer_posted_date":"2026-01-08","last_verified_at":"2026-10-29T03:05:17Z","board_verified":true,"closed_at":null,"days_open":293,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":292},"description":"About Tensorlake Inc\nTensorlake is a serverless platform for agentic applications and Document Ingestion. It provides foundational infrastructure such as serverless compute, code sandboxes, durable execution and unstructured data ingestion APIs to developers in enterprises. We aim to push the frontier of AI that understands real, complex documents and detailed workflows - not just text extraction, but contextual reasoning and scalable application systems.\nThis role is for an experienced scientist who thrives both in innovating foundational models and building usable, robust AI systems for real users.\nWhat You’ll Do\nResearch & Model Development\nLead design and experimentation on state-of-the-art models for document understanding, multimodal reasoning, and deep content extraction.\nResearch, evaluate, and integrate the latest vision-language models (VLMs), retrieval frameworks, RAG systems, and grounding techniques to drive product impact.\nDevelop new benchmarks, datasets, and evaluation methodologies tailored to real-world document AI tasks.\nApplied AI & Product Impact\nBuild and ship production-ready AI components (e.g., table extraction, structure parsing, semantic indexing, multimodal QA, agentic workflows).\nWork with engineering and product partners to deploy models at scale - from prototype to integrated platform features.\nCollaborate closely with customers and partners to prioritize and validate use cases, including LLM orchestration, context engineering, and agent integration.\nQualifications\nTechnical Expertise\n5+ years experience in AI/ML research and applied systems; strong record of delivering results.\nDeep background in document understanding, multimodal AI, NLP + computer vision integration.\nHands-on experience with vision-language models, RAG frameworks, context-aware retrieval, agentic AI, and embedding-based systems.\nApplied AI & Engineering\nTrack record of “research → product” delivery: turning prototypes into robust pipelines, APIs, or services.\nExperience optimizing and fine-tuning large models, knowledge of quantization/LoRA/efficient training.\nProficiency with deep learning frameworks (PyTorch preferred), Python, and scalable ML tooling.\nOptional (but Highly Valued)\nExperience with open-source frameworks and community contributions\nBackground in agents, RAG architectures, retrieval systems, and context engineering.\nExperience designing benchmarks, quality metrics, or curated datasets for complex tasks.\nWhat Sets You Apart\nYou balance deep technical curiosity with product focus and can speak fluently to both ML research and engineering issues.\nYou’ve shipped models and systems that are used by developers or customers in real scenarios (not just research demos).\nYou are comfortable working in a fast-moving startup environment where priorities evolve and innovation is part of the culture.\nBenefits\n- Ability to save in 401(k) plans\n- Comprehensive Healthcare and Dental Benefits\nWhy Tensorlake?\nWe’re building something foundational for how companies integrate AI into knowledge workflows, RAG pipelines, document agents, and real-world enterprise applications. If you’re excited about shaping AI products that matter, and you enjoy bridging research with engineering execution, this is the role to join. 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