At Hark, we are building the most advanced personal intelligence in the world.
About the role
We are looking for a Member of Technical Staff, Post-Training to lead the development of post-training strategies that define how our models acquire coding, computer use, and agentic capabilities at scale.
What they're looking for
- Strong background in machine learning, with hands-on experience training or fine-tuning large models — LLMs, multimodal, or equivalent systems
- Deep understanding of reinforcement learning: policy optimization, reward design, exploration, and the interplay between environment design and agent behavior
- Experience building or working within simulation or execution environments (e.g., code interpreters, sandboxed execution, game environments, robotics simulators)
- Proven ability to design and execute rigorous experiments, with strong intuition for diagnosing training failures and scaling bottlenecks
- Proficiency in Python and PyTorch, comfort working across research and systems code
- Ability to work in a fast-moving, research-forward environment where the right approach is often unknown at the outset
More about this role
Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory.
We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world.
To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems.
We are looking for a Member of Technical Staff, Post-Training to lead the development of post-training strategies that define how our models acquire coding, computer use, and agentic capabilities at scale.
This role sits at the frontier of a rapidly emerging discipline — one where reinforcement learning, simulation, and large-scale model training converge to produce agents that can reason, plan, and act over long horizons. There is no established playbook here. We're looking for...
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