Agentic AI Researcher

Human Resources•Toronto, ON

2d•Hybrid

About The Position

Toronto’s AI Lab is seeking an Agentic AI Researcher to help shape the next generation of LG Electronics’ products by advancing research in agentic AI systems and applying those capabilities across LG’s global product portfolio. In this role, you will contribute to the development of self-learning, autonomous, and personalized AI agents that are embedded directly into LG products, including smart home platforms, connected appliances, and future intelligent devices. Your research will help enable products that can reason, adapt, remember, and proactively assist users over long periods of time. Working closely with senior and lead researchers, engineers, and product teams, you will apply agentic AI concepts—such as lifelong learning, hierarchical memory, and personalized inference—to real-world use cases at scale. Research outcomes will be transitioned into prototypes and, over time, into production systems deployed across millions of consumer and enterprise products worldwide. This role offers a unique opportunity to bridge cutting-edge AI research with tangible product impact, helping define how autonomous and agent-based AI becomes a core intelligence layer across LG’s future smart home ecosystem and beyond.

Requirements

PhD in Machine Learning, Artificial Intelligence, or related field (no post-graduate research experience required), OR Master’s Degree in ML/AI with 3+ years of relevant post-graduate experience
Strong foundational knowledge in machine learning, reinforcement learning, LLMs, memory-augmented models, or agentic AI systems
Hands-on experience with PyTorch and/or JAX; familiarity with large-scale training or distributed systems is a plus
Experience or coursework involving RL reward design, agent evaluation, or long-horizon reasoning tasks
Ability to design experiments, analyze results, and iterate on models and agents
Research experience demonstrated through academic projects, industry work, publications, or open-source contributions
Interest in contributing to academic publications and collaborative research
Strong written and verbal communication skills
Eagerness to learn from senior researchers and grow toward technical leadership
Curious, self-motivated, and capable of working effectively in a collaborative research environment
Demonstrated ability to apply creative problem-solving to real-world challenges

Nice To Haves

Familiarity with large-scale training or distributed systems

Responsibilities

Conduct research on continual learning agents, including skill libraries/tools and curriculum or goal discovery, under the guidance of senior researchers.
Contribute in a fast-paced, technically challenging environment by proposing and testing innovative ideas and solutions.
Design and experiment with self-learning agent systems for lifelong skill acquisition and evolution, including reward functions for task completion quality and long-horizon consistency.
Research and implement techniques for personalized inference control, such as activation steering or persona vectors, with attention to user alignment and enterprise safety.
Contribute to Agent–User–Memory frameworks, including memory agents, user modeling, and orchestration layers for personalization.
Collaborate with academic and internal research partners; support paper writing and experimentation for top-tier conferences.
Translate research ideas into working prototypes and proof-of-concepts.
Implement and evaluate research baselines and metrics for long-context reasoning, planning, memory utility, and skill retention.
Tune and optimize large multimodal models and assist in building evaluations to measure agent and model performance.