Applied AI Engineer job opportunity at MLabs.

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Date2026-02-18T08:05:20.699Z bot
MLabs Applied AI Engineer
Experience: 2-years
Pattern: full-time
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loacation New York, United States Of America
loacation New York....United States Of America

Applied AI Engineer Location:  San Francisco, New York, or Remote USA Onsite/Remote| Full-time Our client is a high-growth, venture-backed organization (YC W24) that has raised over $35M to pioneer the development of autonomous "AI employees." Moving beyond the era of simple chatbots and copilots, our client builds digital workers capable of performing end-to-end professional roles—conducting research, managing outbound communications, navigating deliverability infrastructure, and handling complex objection management autonomously. Currently at $8M+ ARR and scaling rapidly, the organization is seeking its third Applied AI Engineer to join a foundational team. This role is central to pushing the boundaries of autonomous agent behavior and will directly influence the product direction during the development of their next-generation AI platform. The engineering challenges involve managing enormous surface areas and solving complex problems related to multi-step reasoning, real-time adaptation, and human-like interaction. Key Responsibilities Model Orchestration: Evaluate and select appropriate LLMs for specific tasks, balancing the critical trade-offs between cost, latency, reliability, and accuracy. Agent Architecture: Design and implement prompt frameworks and agent behaviors for core workflows, including email generation, meeting scheduling, and prospect research. System Optimization: Refine multi-step agent chains utilizing Retrieval-Augmented Generation (RAG), web search integrations, and complex tool use across CRMs and various APIs. Infrastructure Design: Drive decisions regarding routing, orchestration, evaluation loops, and persistent memory across the agent ecosystem. Trust and Safety: Build robust safety controls and guardrails into agents, partnering with the product team to establish success metrics and fail-safe mechanisms. Emerging Modalities: Explore and deploy novel technologies, including voice AI and multi-modal reasoning, to enhance the "human-like" capabilities of the autonomous workers. Strategic Workflows: Develop agent workflows that make autonomous strategic decisions, self-optimize over time, and deliver measurable business outcomes. Interview Process Recruiter Screening: An initial conversation to discuss background and organizational fit. Leadership Introduction: A 15-minute introductory interview with the CEO. Take-Home Assignment: A practical exercise designed to evaluate technical application. Technical Review: A 30-minute review of the take-home assignment with the CPTO. Values Assessment: A final 30-minute culture and values interview with the CEO. Experience: 2+ years of experience shipping production-ready AI products, ideally within an app-layer AI startup or on a foundation model team. Technical Expertise: Deep hands-on experience with autonomous agents, function calling, RAG pipelines, or self-healing workflows (e.g., LangChain, ReAct, OpenAI Tools, or equivalent). Prompt and Retrieval Systems: A strong background in prompt design, chaining, and retrieval systems. Experience with OpenAI’s RAG and web search tools is considered a significant advantage. Production Management: Proven ability to manage the latency, reliability, and cost of LLM-powered systems at scale. Execution Mindset: The ability to merge a researcher’s curiosity with an engineer's drive for execution—exploring novel techniques while moving quickly to get them into the hands of users. Communication: Exceptional communication skills, with the ability to articulate complex AI concepts to both technical and non-technical stakeholders.

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