Research Interns put inquiry and theory into practice, collaborating with researchers and contributing to AI agent development.
Compensation
The base pay range for this internship is USD $6,810 - $13,480 per month. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $8,920 - $14,610 per month.
Responsibilities
Conduct research and author peer‑reviewed publications
Build and prototype AI agent systems
Evaluate and improve agent performance
Collaborate across research and product teams
Present findings and contribute to community life
Requirements
Doctorate Degree program in Computer Science, Software Engineering, Electrical Engineering, or related STEM field
Nice to have: At least 1 year of research experience
Nice to have: Publications in top venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, COLM, AAAI, IJCAI, KDD, or SIGIR
Lead research on hardware intelligence systems, developing evaluation frameworks, training pipelines, and agent integration for physical engineering contexts.
Responsibilities
Find where our agents fail on real hardware work
Turn failures into evals, environments, and training data
Build graders and verifiers that check agent work against measured data
Fix issues by adjusting prompts, tools, or context, fine‑tuning, running RL, or distilling models
Prototype across research bets such as hardware context for agents, computer use in engineering tools, linking simulation to physical tests, coordinating collections of agents, and edge intelligence
Requirements
Nice to have: post‑training (SFT, RL from feedback, distillation)
Setting the research agenda for Hardware Intelligence, building agents that reason over hardware context, and leading the post-training of AI models.
Responsibilities
Set the research agenda for Hardware Intelligence: pick the problems, place the bets, and define what winning looks like each quarter.
Figure out how to store and represent hardware context, so agents reason over telemetry, test campaigns, requirements, designs, and simulations instead of retrieving them.
Teach agents to work the tools engineers already use, from CAD to simulation to analysis software, through computer use.
Connect simulation to the physical world: calibrate simulations against measured results, and use simulations to plan and explain tests.
Build distributed intelligence: collections of agents that coordinate across programs, fleets, and teams.
Electrical Service Specialists are responsible for the I&C on our power distribution equipment at our Hyper-Scale Data Center sites. Scope includes installation, testing, operation, troubleshooting, and modification of customer equipment and systems.
Locations (3)
Lakewood, Colorado, United States
Denver, Colorado, United States
Cheyenne, Wyoming, United States
Compensation
The pay range for this position is $57,120 - $107,590 annually.
Responsibilities
Lead basic to complex installation and checkout of electric and electronic components for systems and service installed jobs and service agreements.
Perform maintenance and testing of switchgear/switchboard, motor controls, circuit breakers, panel boards, transformers, and surge protection devices.
Conduct vibration analysis and metering, battery maintenance and testing, power monitoring, power quality, and protective relay maintenance.
Looking for post-training researchers and engineers to contribute to synthetic data, RL environments, and evaluation to close the loop between AI models and hardware chips.
Responsibilities
Design and iterate on SFT and RL recipes for large-scale models, with a focus on compute efficiency and steep, stable RL climbs.
Build and scale synthetic data pipelines, including generation, quality filtering, and coverage metrics.
Create long-horizon synthetic environments and task curricula for agentic coding and reasoning.
Make RL stable at scale through careful recipe and stability work.
Build evaluations that track agentic capability, catch regressions, and drive recipe decisions.
Requirements
Degree in CS, EE, math, or a related field, or equivalent practical experience
Nice to have: Research track record in RL, NLP, or machine learning
Nice to have: publications at NeurIPS, ICML, ICLR, or COLM
Nice to have: Experience building RL environments for complex frontier coding and agentic benchmarks
Nice to have: Shipped result: post-training run, data recipe, model release, or research outcome
Support the CTO as a right-hand assistant, managing AI agent deployment, pipeline development, and data engineering tasks in an international logistics environment.
Responsibilities
Deploy and manage AI agents across international geographies
Build and maintain non-deterministic LLM pipelines
Create and evaluate RL environments for agent training
Design, build, and deploy intelligent systems that power Glance’s personalized lock screen and live entertainment experiences, blending deep ML craftsmanship with autonomous agentic AI systems.
Responsibilities
Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning
Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding
Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact
Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts
Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities
Requirements
Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields
PhD is a plus
Nice to have: Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems
Nice to have: Experience in large-scale recommendation systems or personalization
Nice to have: Experience with reinforcement learning, contextual bandits
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