AI Engineer – R&D

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🕒 Maio 14

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LottieFiles

SiteLinkedInTodas as Vagas

51 - 200 funcionários

☁️ SaaS

💰 $37.000.000 Series B em 2022-05

Design • SaaS • Animation

A LottieFiles é uma plataforma líder que permite a designers e desenvolvedores criar, editar, colaborar e implementar animações Lottie leves em sites, aplicativos e plataformas de mídia social. A plataforma é utilizada por mais de 9,5 milhões de designers e desenvolvedores de mais de 280. 000 empresas em todo o mundo. A LottieFiles oferece uma ampla gama de ferramentas e integrações com ferramentas populares de design e desenvolvimento, como After Effects, Figma, Webflow e Canva. Além disso, a LottieFiles fornece recursos aprimorados por IA para aumentar a criatividade e a produtividade, garantindo uma implementação perfeita das animações em qualquer projeto. A plataforma é conhecida por seus runtimes dotLottie de alto desempenho, oferecendo animações escaláveis e interativas que são menores e com carregamento mais rápido em comparação com formatos tradicionais como GIFs.

Descrição

• Design and execute fine-tuning strategies for structured generation and editing workflows. • Build supervised datasets from successful generations, retries, failures, and user edits. • Develop measurable benchmarks for generation quality, correctness, and edit preservation. • Experiment with open-source models such as Llama, Qwen, Mistral, DeepSeek, or related architectures. • Implement LoRA, QLoRA, supervised fine-tuning (SFT), distillation, preference tuning, or synthetic data approaches where appropriate. • Build automated pipelines for collecting, cleaning, evaluating, and promoting production data into training datasets. • Use validation systems, intermediate representations, runtime analysis, and rendered outputs as structured feedback signals for models. • Improve retry, repair, and self-correction workflows for generation pipelines. • Collaborate with engineering and product teams to improve model reliability and output quality.

🎯 Requisitos

• Strong experience building with LLMs or structured generation systems in production or applied research settings. • Hands-on experience fine-tuning or adapting open-source language models. • Strong Python engineering skills. • Experience building evaluation systems, ML experimentation workflows, or data pipelines. • Strong understanding of prompt engineering, structured outputs, tool use, and model failure analysis. • Ability to define measurable evaluation criteria rather than relying only on subjective review. • Comfort debugging systems spanning model outputs, validation systems, runtime behavior, and rendered results. • Strong communication and collaboration skills. • Experience with code generation, DSL generation, or compiler-aware AI systems. • Experience with LoRA, QLoRA, SFT, preference tuning, distillation, or synthetic data generation. • Familiarity with animation systems, graphics pipelines, design tools, SVG, WebGL, shaders, or procedural graphics. • Experience with multimodal or visual-language-model evaluation workflows. • Experience with observability or ML evaluation tooling such as Weights & Biases, Langfuse, MLflow, or OpenTelemetry. • Experience building agentic systems, orchestration pipelines, or multi-step generation workflows. • Familiarity with ASTs, intermediate representations (IRs), or structured program representations.