
KineticAI, Inc. is a forward-thinking research organization specializing in Human-Machine Teaming (HMT). They design solutions that enhance collaboration between humans and machines, focusing on…

KineticAI, Inc. is a forward-thinking research organization specializing in Human-Machine Teaming (HMT). They design solutions that enhance collaboration between humans and machines, focusing on…
Founded: 2019
Headquarters: The Woodlands / Houston, Texas, United States
Team size: 1-10 employees
Focus: Human-Machine Teaming, industrial automation, AI & computer vision
Funding stage: Pre-seed
Improving collaboration and performance between humans and machines in manufacturing and industrial operations.
2019
Data and Analytics
50488 USD
Listed as pre-seed; public profiles indicate one funding round.
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We are seeking an academic researcher to work on cutting-edge projects at the intersection of Large Language Models (LLMs), product management, and enterprise AI adoption.
Required Skills:
· LLM & NLP Expertise: Proficiency in prompt engineering, retrieval-augmented generation (RAG), constrained decoding, JSON schema–based outputs, and evaluation harness design.
· Systems & Integration: Experience with function-calling APIs, multi-agent systems, MCP connectors, and enterprise tool integration.
· AI Safety & Governance: Strong understanding of responsible AI, calibration of model outputs, over-trust mitigation, privacy-preserving policies, and regulatory compliance.
· Experimentation & Analytics: Ability to design and automate experiment workflows, evaluate performance trade-offs, and derive actionable insights for product decisions.
· Software Engineering: Solid coding skills (Python, JavaScript, or similar), version control, and building scalable, auditable workflows.
· Applied ML & Multimodality: Knowledge of multimodal assistants (LLM + CV for video Q&A), retrieval strategies, and enterprise-grade AI deployment.
· Product Thinking: Strong grasp of product management concepts, decision-making workflows, and user-centric AI applications.
Must-have: Prior publications
To apply:
Share your prior research works, such as Google Scholar or ORCID.
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