At Octopyd, we’re not just automating hiring, we’re building modular AI agents that transform how companies make decisions across talent acquisition, workforce evaluation, and enterprise operations. As an Applied Scientist, you’ll work at the intersection of AI, human decision-making, and business operations. You’ll design novel algorithms that combine reinforcement learning, large language models, and expert knowledge graphs to solve complex real-world hiring and workforce optimization challenges. If you're passionate about advancing applied AI research and shipping production-grade intelligent systems, this is your opportunity.
What You'll Do
- Develop next-generation decision-making models: Design and implement algorithms that integrate RL, supervised learning, LLMs, and knowledge-based reasoning to evaluate candidates, optimize hiring workflows, and assist human decision-makers.
- Bridge research and production: Work alongside AI engineers to deploy experimental models into scalable, production-ready microservices that operate in real-world recruiting pipelines.
- Build robust evaluation pipelines: Develop data processing, evaluation frameworks, and automated experimentation platforms to continuously improve model accuracy, fairness, and explainability.
- Analyze and iterate: Leverage large-scale datasets including resumes, job descriptions, interviews, assessments, and human evaluation feedback to fine-tune model behavior and surface actionable insights.
- Contribute to research community: Document findings, publish technical reports, contribute to open-source initiatives, and present work internally and externally to advance Octopyd’s AI research leadership.
What You Bring
- PhD or Master's in Computer Science, Applied Mathematics, Operations Research, or a related field with focus on applied ML, RL, optimization, or decision systems.
- Experience developing ML/RL algorithms applied to real-world data — ideally demonstrated via publications, open-source projects, or applied industry work.
- Proficiency in Python and deep learning frameworks (e.g. PyTorch, TensorFlow), with strong software engineering fundamentals.
- Excellent communication skills, with ability to translate complex research into actionable business and product outcomes.
Bonus Points
- Experience with AI for human evaluation, skill matching, recommender systems, or workforce analytics.
- Experience with multi-agent systems, game theory, or reinforcement learning for decision-making.
- Prior work with large-scale LLM fine-tuning, knowledge graphs, or explainable AI.
- Familiarity with HRTech, recruiting workflows, ATS integrations, or enterprise SaaS platforms.
Seniority level
Seniority level
Mid-Senior level
Employment type
Employment type
Full-time
Job function
Job function
Engineering and ResearchIndustries
Software Development
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Medical insurance
Vision insurance
401(k)
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