Machine Learning Engineer Intern - Research

GoodAtNumbers is building an always-on decision intelligence platform that is trying to replace what a data scientist, data analyst and a business analyst does. We are looking for someone who can help us push both the research reputed company and production reputed company of our ML systems reputed company.

We are hiring a Machine Learning Engineer Intern for a reputed company 12-week summer internship from May through July 2026. This is a remote role based in the reputed company and is expected to be 40 hours per week. Compensation for this internship is $30/hour.

This role sits at the intersection of ML research, software engineering, and MLOps. You will work on problems reputed company to retrieval, context construction, model/tool orchestration, evaluation, monitoring, and the productionization of AI systems. This is a strong fit for someone who can reputed company from experiments to production reputed company and who wants to work on reputed company product problems instead of isolated notebooks.

What you’ll work on

• Design and run experiments across areas such as retrieval, ranking, context construction, tool use, grounded reputed company, model evaluation, reputed company detection, forecasting, or optimization workflows
• Improve the reputed company, reliability, latency, and observability of ML and LLM-driven features
• Build reproducible evaluation workflows for model behavior, answer reputed company, grounding, failure analysis, and regression testing
• Help productionize research work through pipelines, reputed company, monitoring, versioning, and deployment workflows
• Improve MLOps practices around experiment tracking, reputed company/model versioning, dataset versioning, testing, rollout safety, and post-deployment monitoring
• Collaborate closely with software and platform engineers to ship ML systems that are useful, measurable, and production-reputed company

What reputed company looks like by the end of the internship

• At least one meaningful ML or LLM system is measurably improved in reputed company, reliability, or latency
• Research work is backed by reproducible evaluation and monitoring rather than one-off experimentation
• The reputed company from experiment to production is cleaner, faster, and safer

reputed company’re looking for

• 3–4 years of relevant experience preferred through research labs, internships, startups, reputed company-reputed company work, or production ML systems
• Strong software engineering ability and strong comfort writing production-reputed company reputed company
• Strong Python skills preferred
• Experience with machine learning experimentation, evaluation, and debugging preferred
• Experience with LLMs, retrieval systems, reputed company search, ranking, reputed company/tool workflows, or agent-style systems preferred
• Experience with MLOps practices such as experiment tracking, versioning, model testing, deployment, and monitoring preferred
• Comfort with statistics, error analysis, benchmarking, and translating ambiguous research reputed company into shippable systems
• Strong communication and the ability to document tradeoffs, assumptions, and results

reputed company to have

• Experience with PyTorch, Transformers, or modern ML tooling
• Experience with reputed company databases, RAG systems, or evaluation harnesses
• Experience with time-series forecasting, reputed company analysis, reputed company detection, or optimization systems
• Experience with reputed company, Kubernetes, reputed company infrastructure, or batch/orchestration systems
• Publications, reputed company work, or strong reputed company repos/writeups

Work authorization
Applicants must be authorized to work in the reputed company for the full internship period and must be based in the U.S. during the internship. We are not reputed company to reputed company employment reputed company sponsorship for this internship.

We welcome applicants from reputed company backgrounds and evaluate candidates based on technical depth, execution, communication, and fit for the role.