LLM Machine Learning Engineer

Overview intellicia brings a new paradigm to the B2B market with innovative AI technology. It was founded by the co-CEO and early members of Dable, who scaled personalized recommendation services to thousands of media outlets across seven Asian countries and sold the company for $140M.

This position is responsible for directly improving the performance of TheSurvey.ai's core AI response generation model and applying the latest LLM technologies to the product.

What You'll Gain

• Fine-tuning experience with diverse open-source LLMs (Llama, Mistral, Gemma) using SFT, LoRA, DPO and performance optimization
• Know-how in building and processing high-quality datasets for LLM tuning
• Experience packaging tuned models for production serving and deploying as APIs
• Building MLOps pipelines spanning experimentation, deployment, and monitoring
• Developing LLM models specialized for market research and consumer surveys

Key Responsibilities

Responsibilities are adjusted based on core competencies and growth potential. You won't necessarily do all of the below.

LLM Fine-tuning (Required)

• Fine-tuning and evaluating open-source LLMs (Llama, Gemma, etc.)
• Designing, collecting, cleaning, and processing purpose-built tuning datasets
• Researching latest LLM trends and evaluating service applicability

Model Serving & MLOps (When Applicable)

• Developing fine-tuned models into servable API endpoints
• Optimizing inference speed and cost efficiency (Quantization, vLLM, etc.)
• Designing and building pipelines to automate training, evaluation, and deployment
• Introducing and operating experiment & model versioning systems (e.g., MLflow, DVC)
• Monitoring serving model performance and configuring retraining loops

Requirements AI / LLM Development Experience

• 3+ years of hands-on AI/ML experience with Python
• LLM fine-tuning project experience using Hugging Face Transformers, PEFT, etc.
• Data preprocessing and processing skills for model training (PyTorch, Pandas, NumPy)

Smooth Collaboration with Business Teams

• Actively embrace business requirements.
• Even when specs are incomplete, collaborate by understanding the intent and driving toward a shippable outcome.

Preferred Qualifications

• Deep data analysis: ability to statistically analyze model performance and experiment results
• MLOps: CI/CD/CT pipeline experience (Kubeflow, MLflow, Airflow, etc.)
• Model serving framework experience (Triton, BentoML, vLLM, etc.)
• Cloud: Experience training and deploying ML models on GCP, AWS, etc.
• RAG: Experience developing or improving RAG systems
• Optimization: Large model inference optimization (Quantization, Pruning, etc.)
• Research: Enjoys quickly understanding latest papers and implementing them in code

Benefits & Perks

• Grow alongside the product at an early-stage company
• Flexible work environment
• Education support for professional development

Hiring Process

1

Application Review

Resume screening

2

1st Online Interview

Video Interview

3

2nd In-Person Interview

In-Person Interview

4

Final Offer

Offer notification

How to Apply