Abundant
AI
SWEIntern
Neural analysis suggests this role is
optimal for Entry candidates.
“SWE Intern at Abundant. Skills: LLM research, evaluation, benchmark design, AI coding agents, Python, data science, machine learning. Support the development of customer-facing products and internal research tooling. Research, evaluation, and benchmark design”
What You'll Achieve.
Maintain high data quality standards
Industry & Context.
Ability to navigate experimental ambiguity
What They're Looking For.
Must Have
Proficiency with AI coding agents and development tools, Foundational knowledge in Python, data science, or machine learning, Extremely clear communication, Pragmatism, Velocity, Curious about AI and keeping up with the latest papers in agents, RL and benchmarks, Impact-oriented, Hacker, Ability to navigate experimental ambiguity and create novel approaches
Nice to Have
Large language model (LLM) research and effectiveness studies, Evaluation and benchmark design for AI agents
What You'll Do.
Support the development of customer-facing products and internal research tooling
Assist in tasks related to the Core Platform
including core simulation engines
data creation tooling
and experimentation platforms
Benchmarking and evaluation tasks to help the team maintain high data quality standards
Coding and executing experiments
Improving simulation performance
Transition core features into more modular components
Handle large-scale event processing
How You'll Work.
Team & Collaboration
Work closely with our engineering team and founders
Communication Scope
Extremely clear communication; Explain very complex technical concepts in very simple terms
Full Job Description
ABOUT ABUNDANT AI models rely on two fundamental ingredients: compute and data. Abundant is building the NVIDIA of training data. NVIDIA, the leader in compute, has a peak market cap of $5T and generated $130B in revenue last year as the need for scaling compute has exploded. We believe the need to scale data is just beginning, as we move beyond SFT and human supervision to RL and Learning from Experience. Our founding team consists of former founders, ML engineers, roboticists and data leads from Waymo, Google, Mercor and AWS. Our team has previously worked with DeepMind to deploy deep learning models at 1B user scale, trained SOTA models for self-driving at Waymo, and scaled data pipelines of tens of thousands of human annotators at YouTube. Our pioneering work in human computation, synthetic data, simulation and RL give us the advantage in delivering results to our customers. Why now? Training data is more important and more scarce than ever before. Scaling laws dictate that linear improvement in model performance demands an exponential increase in training data. But there is only one World Wide Web and most of it has already been trained on. The next advances will require major advances in simulation, synthetic data and learning from experience. What happens if we succeed? Abundant will be the core enabler for not only AGI, but ASI and physical intelligence. Most of the challenges in model algorithms and compute are already solved. What’s missing? The data necessary to move from general knowledge to domain expertise; from chatbots to agents; and from text to multimodal and physical AI. Ask any AI researcher or roboticist: the core bottleneck to progress is the availability of data, hence “_abundant data_”. Abundant works with a majority of the top AI labs, as well as frontier startups and F500 enterprises. ABOUT THE ROLE As a Software Engineering Intern (Research Focused), you will work closely with our engineering team and founders to support the development of
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