Why This Job is Featured on The SaaS Jobs
This Research Intern role sits at the intersection of SaaS and applied AI, where reinforcement learning is being tied directly to customer experience workflows. The listing points to a productized platform spanning conversation intelligence, multimodal understanding, and agentic systems, which is a distinctly SaaS-shaped problem space because model performance must translate into reliable behavior across real customer interactions.
For a SaaS career, the standout value is exposure to the full loop between research and production. Work such as defining reward signals, structuring interaction traces into training datasets, and evaluating systems with real-world feedback mirrors how modern SaaS companies operationalize machine learning. That experience tends to transfer well across AI-enabled SaaS roles because it builds intuition for instrumentation, iteration cycles, and the practical constraints of deploying learning systems.
The role is best suited to early-career candidates who want hands-on ownership of experiments and are comfortable moving between theory and implementation. It also fits someone who prefers concrete problem framing, measurable outcomes, and collaboration with engineering and product partners to land research work in shipped software.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
🚀 Build the next generation of Agentic AI with us
Our platform combines conversation intelligence, multimodal understanding, and agentic AI systems to power both human agents and autonomous AI agents across the entire customer experience lifecycle.
A core part of this vision is our investment in custom Small Language Models (SLMs)—purpose-built for CX workflows—paired with reinforcement learning systems that continuously improve decision-making in real-world environments.
We’re looking for a Research Intern (Reinforcement Learning) to join us in shaping this future.
What you’ll do
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Design and build reinforcement learning environments that model real-world customer interaction workflows.
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Design RL agents that learn from these environments using real-world interaction data, rewards, and feedback loops
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Define reward models and feedback loops using real-world signals (outcomes and human feedback)
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Enable learning from production data by structuring interaction traces into training-ready datasets for offline and online learning
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Experiment with multi-agent systems and simulation frameworks for complex coordination and decision-making
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Collaborate with engineering and product teams to deploy, evaluate, and iterate on learning systems in production at scale.
What we’re looking for
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Currently pursuing (or recently completed) a degree in Computer Science, AI, Machine Learning, or related field
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Strong understanding of reinforcement learning fundamentals
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Familiarity with RL environments and training libraries such as Verl and Tinker
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Strong foundation in probability, math, and optimization
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Passion for building real-world AI systems
Nice to have
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Experience with RLHF, LLM/SLM fine-tuning, or model alignment
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Exposure to agent-based systems or multi-agent RL
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Prior research, projects, or publications in RL or applied ML
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Experience working with large-scale or production datasets
Why Level AI
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Work on production-grade Agentic AI systems used by leading enterprises
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Build alongside a team with deep expertise from Amazon, Google, and Meta
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Be part of a fast-growing Series C AI company.
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Direct exposure to 0→1 AI innovation in CX and decisioning systems
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