AI Engineer (R&D) - LottieFiles

Fully remote position

Candidates should have at least 4 hours of overlap with the Malaysia time zone (MYT / UTC+8)

Direct hire opportunity

Visa sponsorship is not available for this role

About the Company
We are partnering with a fast-growing technology company building next-generation AI-powered creative tools for animation, design, and interactive media workflows. Their platform is used to help creators and teams accelerate visual content production through intelligent generation, editing, and automation systems.
The company operates at the intersection of generative AI, structured content systems, and creative tooling, with a strong focus on production-grade AI infrastructure, model reliability, and real-world usability. Their engineering culture emphasizes experimentation, measurable quality improvements, and scalable AI systems deployed directly into user-facing products.

About the Role
We’re looking for an AI Engineer to help build specialized generation, editing, evaluation, and optimization systems for creative and structured content workflows.
This role focuses on structured generation, domain-specific model adaptation, evaluation systems, feedback pipelines, and production AI infrastructure. You’ll work closely with engineering, design, and product teams to improve generation quality, reliability, efficiency, and usability across AI-assisted creative workflows.
This is an opportunity to work on highly applied AI problems involving:
Structured and constrained generation

AI-assisted editing systems

Evaluation and observability pipelines

Fine-tuning and adaptation of open-source models

Multi-step generation and repair workflows

Scalable production AI systems

What You’ll Work On
Natural-language-to-structured-content generation workflows

Structure-preserving editing and modification systems

Validation and repair pipelines for generated outputs

Evaluation systems for quality, correctness, consistency, and runtime performance

Training and evaluation datasets built from production usage and interaction traces

Smaller, lower-latency models for targeted generation, editing, routing, and repair tasks

Multi-step orchestration and self-correction workflows for AI systems

Key Responsibilities
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 cross-functionally with engineering, design, and product teams to improve model reliability and output quality

Required Qualifications
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

Preferred Qualifications
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

Why This Role Is Interesting
Work on real-world AI systems used in creative production workflows

Build beyond prompt engineering into evaluation, repair, optimization, and infrastructure

Help shape production-grade AI systems for next-generation creative tooling

Collaborate with a highly technical and product-focused engineering team

Tackle challenging problems involving structured generation, multimodal systems, and AI reliability