@tyler - Senior AI Engineer

Senior AI Engineer

Artificial Intelligence Engineer

Available for hire

Years of experience

9+ years

Experience level

Senior

Available for

Full-time, Part-time, Contract

Available from

28 Apr 2026

Download Resume / CV

I'm a Senior AI/ML Engineer with 9+ years of experience leading large-scale machine learning systems for gaming AI and social platforms. Currently designing and fine-tuning LLMs to power autonomous agents in real-time gaming environments, and previously built ML models at Meta that served billions of users. Proven expertise across the full MLOps lifecycle, from data engineering to CI/CD deployment on cloud platforms. Seeking to leverage deep LLM and MLOps skills to deliver cutting-edge AI solutions that drive product innovation.

Languages

English

Employment History

Senior AI Engineer, MLOps Engineer at Altera AI 2024 - 2026

• Designed and fine-tuned large language models (LLMs) to power human-like AI agents capable of real-time decision-making, social interaction, and autonomous behavior in gaming environments (Minecraft, Roblox) • Generated professional trajectory datasets by running AlphaZero-based data collection scripts. • Specialized Qwen-family models (4B–30B) for complex OpenSpiel games, producing stable policy improvements against MCTS baselines. • Developed and optimized multi-agent training pipelines supporting simultaneous coordination of up to 1,000 AI agents in live game simulations • Conducted experiments on agent personality modeling, emergent behavior, and cooperative task-solving using reinforcement learning and supervised fine-tuning (SFT) • Built and maintained end-to-end ML pipelines for model training, evaluation, versioning, and deployment on cloud infrastructure using GoLang, TypeScript, Docker, and Terraform, enabling faster model iteration and more reliable releases • Evaluated model performance against industry benchmarks (e.g., Voyager), iterating on training strategies to improve agent intelligence and reduce inference costs • Implemented CI/CD workflows for continuous model training and automated testing, reducing deployment cycles and ensuring model reliability • Monitored model performance in production gaming environments, identifying drift and triggering retraining pipelines as needed • Managed experiment tracking and model registry using tools such as MLflow and Weights & Biases to ensure reproducibility across research iterations • Scaled distributed training across 8×H100 GPUs using DeepSpeed ZeRO-3 with BF16 precision, stabilizing RL training and reducing wall-clock time-to-convergence by a defensible margin. • Collaborated cross-functionally with research, engineering, and product teams to productionize research prototypes into stable, scalable AI systems

Machine Learning Engineer at Meta 2020 - 2023

• Designed, developed, and deployed scalable machine learning models to support Meta's core products including Facebook Feed, and WhatsApp recommendation systems, serving billions of users globally. • Collaborated with data scientists, product managers, and software engineers in a fully remote setting to build end-to-end ML pipelines using GoLang services and Redis for feature storage, accelerating pipeline deployment and enabling faster model iteration • Built and optimized large-scale data processing workflows using Meta's internal tools and frameworks (such as PyTorch, FBLearner Flow, and Spark) to improve model training efficiency and reduce latency. • Contributed to the development and fine-tuning of deep learning models for natural language processing (NLP), computer vision, and content ranking tasks across Meta's family of apps. • Monitored and maintained production ML systems, performing root-cause analysis, debugging, and performance tuning to ensure high availability and reliability at massive scale. • Participated in Meta's AI research initiatives, exploring novel architectures and techniques aligned with the LLaMA and FAIR (Fundamental AI Research) efforts. • Implemented A/B testing frameworks to evaluate model performance improvements, directly impacting user engagement and ad revenue metrics.

Machine Learning Engineer at Emerge 2017 - 2020

• Developed and trained machine learning models for emotion recognition, leveraging multimodal biometric data including facial expressions, physiological signals, and brain activity patterns • Built and optimized signal processing pipelines to interpret ultrasonic sensor data, enabling real-time tactile feedback generation for the company's haptic interface hardware • Collaborated cross-functionally with hardware and product teams to integrate emotion AI capabilities into prototype multi-sensory communication devices • Designed and implemented deep learning algorithms to digitize and classify emotional states, contributing to the core Emotion AI platform • Conducted research on affective computing and human-computer interaction to inform AI model architecture decisions • Maintained and improved data preprocessing workflows, ensuring high-quality training datasets for emotion detection models • Participated in iterative prototyping cycles, testing AI models against real-world haptic and sensory feedback scenarios

Education

Bachelor of Computer Science at Florida State University 2012 - 2016

Senior AI Engineer

Skills

Languages

Employment History

Education

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