Member of Technical Staff – Research Intern
Trajectory
Member of Technical Staff – Research Intern
Trajectory
Continual-learning platform for AI models
As a Member of Technical Staff (Research) at Trajectory, you will design and build the post‑training stack that lets our customers’ models continually learn from real production workflows. You will own end‑to‑end experiments across data, training, and evaluation: shaping telemetry into learnable signals, training and serving custom LLMs and agents, and designing novel algorithms. You’ll work directly with the founders and design partners, make foundational technical and product decisions, and help define both the culture and architecture of a company whose core mission is to enable continually learning intelligence.
Experience training or serving large language models
Experience building RL environments or evaluation suites for LLMs
Proficiency in PyTorch, JAX, or similar ML frameworks
Strong experimental design skills
Master’s / research experience
Background in pre- or post-training RL for LLMs
Experience with high-performance computing or large-scale clusters
Contributions to open-source ML research or infrastructure
Demonstrated technical creativity (research, OSS, side projects)
PhD (last year of PhD), working on AI / RL open source projects
Trajectory is a research and product lab creating the platform for continual learning.
AI is the most capable software ever built, and the least able to learn. Every valuable correction and edit that happens in a product evaporates at the next session. A few teams have closed this gap by hand-coupling their models to their products: Composer, Claude Code, Windsurf SWE-1.
Trajectory is the first scalable approach for every company: our platform unlocks the signal already sitting in product use, so companies can continuously post-train large-scale agentic models that outperform the frontier. Working with leading AI companies, including Decagon, Clay, Harvey, Mercor, and Rogo.
Founded by researchers from Deepmind, OpenAI, Meta, Apple, Amazon, and Scale. $15M led by Conviction, with Fei-Fei Li, Jeff Dean, and founders of Notion, Braintrust, Modal, Hugging Face, and Dropbox.
Continual-learning platform for AI models
Trajectory helps AI-native companies replace static models with an intelligence layer that continually improves by learning from real product usage signals. Its platform turns traces, corrections, re-prompts, and edits into models that get smarter over time, without sacrificing control. The product works by integrating an intelligence layer into existing apps and using user interactions as training data, with customers retaining full control over training data and deployment approvals (SOC 2 certified). Trajectory differentiates itself by offering a full lifecycle for continual learning—not just a single model—along with strong governance and data control, and partnerships with notable customers. Its goal is to make applications that get smarter as people use them, through a scalable, governance-friendly continual learning platform.
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