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
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