Location
Bengaluru, Karnataka
Work Mode
Hybrid
Duration
6 months
Stipend
₹30,000/mo
Start Date
Immediate / Rolling
Openings
7 positions
Type
Full-time
Category
Machine Learning
About the Internship
Capillary Technologies is hiring a Reinforcement Learning Intern in Bengaluru — a hands-on, mentor-led internship built for students and 2026 freshers who want real exposure to reinforcement learning rather than a certificate-only programme.
About the role
As a Reinforcement Learning Intern at…
About Capillary Technologies
Capillary Technologies offers a mentor-led Reinforcement Learning Intern in Bengaluru with live project work in machine learning, a stipend of ₹30,000/month and a Pre-Placement Offer opportunity for strong performers.
See all Capillary Technologiesinternships →Bengaluru, KarnatakaVerified employer7 openings
Apply to Capillary Technologies →Skills Required
Reinforcement LearningReward DesignPythonPyTorchOpenAI GymPolicy GradientsSimulationExperiment TrackingMathematicsEvaluation
Who Can Apply
- Final-year students and recent graduates from B.E./B.Tech (CSE/IT/ECE), MCA or M.Sc (Computer Science / Mathematics / Statistics) who are based in or willing to work from Bengaluru. You should be keen to learn Reinforcement Learning, Reward Design, Python on real projects and able to write clearly. Freshers are welcome — no prior work experience is required, and a strong project portfolio matters more than a long CV.
Perks
Exposure to professional tools and workflowsPre-Placement Offer (PPO) opportunityFlexible working hoursNetworking with senior professionalsInternship CertificateDirect access to the team that owns the data you work on
Interview & Selection Process
4 rounds — typical selection flow for this role:
- 1Application screening & resume shortlisting by the talent team
- 2Technical interview on fundamentals and your projects
- 3Practical assignment or case study with a short walkthrough
- 4HR discussion — stipend, start date, expectations and offer
₹30,000/month
Stipend · 6 months
Openings7 positions
Applicants0 applied
ModeHybrid
CategoryMachine Learning
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