Senior Machine Learning Engineer

Artificial Agency
Location: Edmonton, Alberta (Remote possible for exceptional candidates)

Job Description

Artificial Agency, the leading player-facing agentic AI company for games, is seeking an experienced Senior Machine Learning Engineer to develop and improve the models behind our Behavior Engine. You will train, fine-tune, evaluate, and optimize large and small language models for agents that perceive, reason, and act in real-time interactive environments.

This is a senior individual contributor role for someone who can take a model improvement from an initial hypothesis through implementation, evaluation, and production integration. The successful candidate will combine practical machine learning experience, strong software engineering skills, and sound experimental judgment to improve agent capabilities within the latency, memory, and cost constraints of games.

You will work across model development, training data, and inference performance. This includes investigating model failures, adapting research methods to our workloads, and measuring whether improvements hold across representative game scenarios.

Artificial Agency is building agentic AI for games, and this role will help make intelligent agents more capable, responsive, and economical to run. You will work with a team of ex-DeepMind researchers and world-class game developers on a new category of AI-powered entertainment.

Responsibilities

• Develop and fine-tune large and small language models for game-agent tasks, including interpreting game information, following instructions, and interacting with players and other agents.

• Build and maintain data preparation and training workflows. Curate training examples, investigate data quality, and maintain appropriate separation between training and evaluation data.

• Design reproducible experiments with clear hypotheses, baselines, and evaluation criteria. Compare candidate models across agent capability, reliability, latency, memory use, and inference cost.

• Investigate failures with the Agents and Game Technology teams. Use representative scenarios and diagnostic evidence to identify where changes to training data, model behaviour, or agent interfaces could improve outcomes.

• Partner with Platform engineers, prototype and benchmark models using serving frameworks such as vLLM, NVIDIA Triton, or equivalent tools to assess performance under representative workloads.

• Translate relevant research into tested implementations. Document methods, results, and limitations, and share findings through code reviews and technical guidance.

You are accountable for the model-development work, experimental evidence, and reusable training workflows you deliver. Production inference operations and shared evaluation infrastructure have dedicated engineering owners; you will work together on integration, validation, and diagnosing issues.

Requirements

• An AI-first approach to engineering, with hands-on experience using AI tools for development, testing, debugging, or analysis. You are excited to make AI-driven workflows central to how you work, actively experiment with new capabilities, and adapt your approach as tools improve, while taking responsibility for the quality of what you deliver.

• 5+ years of experience in machine learning engineering or a closely related discipline, including substantial hands-on work fine-tuning and deploying language models.

• Strong programming skills in Python and experience with PyTorch, JAX, TensorFlow, or comparable machine learning frameworks.

• Practical experience with language-model training and post-training methods, including supervised fine-tuning and preference optimization or reinforcement learning.

• Experience building reproducible experiments, preparing training data, evaluating models, and investigating regressions.

• A strong understanding of model-performance trade-offs, including latency, throughput, GPU memory, inference cost, and model quality.

• Experience with model-serving frameworks such as vLLM, NVIDIA Triton, or equivalent tools, and the ability to collaborate with infrastructure engineers on production integration.

• Sound software engineering practices, including testing, version control, code review, and maintaining tools that other engineers can use.

• Ability to independently lead technical investigations and explain findings and trade-offs to researchers, engineers, and game developers.

• A Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field is preferred; equivalent practical experience is also valued.

• Experience with multimodal models, model distillation, quantization, or on-device inference is valuable. An interest in games and how agent behaviour affects player experience is important.

• Comfort working in a fast-moving startup environment where systems, responsibilities, and best practices are still being defined.