Good At Numbers

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 hiring a strong software engineering intern to help turn that architecture into polished, reliable product experiences. We are hiring a Software Engineer Intern (Product & Backend) for a paid 12-week summer internship from May through July 2026. This is a remote role based in the United States and is expected to be 40 hours per week. Compensation for this internship is $23/hour. This role is best for someone who enjoys building product features end-to-end, writing clean backend logic, improving code quality, and shipping systems that users can rely on. You will help build and refine core workflows across the platform while working closely with the rest of the engineering team. What you’ll work on - Build product features across areas such as workspace setup, dataset and document workflows, semantic model workflows, metrics, investigations, evidence views, decision workflows, and operator-facing product surfaces - Design and implement backend services, APIs, data models, and application logic - Improve code quality through testing, refactoring, documentation, and better engineering practices - Help connect deterministic tools and backend systems to user-facing workflows in a reliable and maintainable way - Improve performance, usability, and overall engineering quality in the core product - Collaborate across product, platform, and ML workstreams to ship production-ready features What success looks like by the end of the internship - Meaningful product features are shipped to a high standard - Core backend code is cleaner, better tested, and easier to extend - The product becomes more polished, more reliable, and more ready for production use What we’re looking for - 3+ years of relevant software development experience preferred through internships, startups, research labs, open-source work, or production projects - Strong software engineering fundamentals, problem-solving ability, and ownership - Experience with backend development, APIs, and relational databases preferred - Experience with modern frontend frameworks and/or full-stack application development preferred - Experience with testing, code review, debugging, and maintainable application design preferred - Experience with Python and/or JavaScript/TypeScript preferred - Comfort working in ambiguous product environments and translating product ideas into shipped software - Strong communication and execution Nice to have - Experience with React, Next.js, FastAPI, Django, Node.js, or similar frameworks - Experience with Postgres, workflow systems, dashboards, or analytics/data products - Experience with auth, permissions, auditability, or evidence-heavy enterprise workflows - Experience building AI-enabled product features or working alongside ML systems Work authorization Applicants must be authorized to work in the United States for the full internship period and must be based in the U.S. during the internship. We are not able to provide employment visa sponsorship for this internship. We welcome applicants from all backgrounds and evaluate candidates based on skills, execution, communication, and fit for the role.

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 quality and production quality of our ML systems forward. We are hiring a Machine Learning Engineer Intern for a paid 12-week summer internship from May through July 2026. This is a remote role based in the United States 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 related to retrieval, context construction, model/tool orchestration, evaluation, monitoring, and the productionization of AI systems. This is a strong fit for someone who can move from experiments to production code and who wants to work on real 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 generation, model evaluation, anomaly detection, forecasting, or optimization workflows - Improve the quality, reliability, latency, and observability of ML and LLM-driven features - Build reproducible evaluation workflows for model behavior, answer quality, grounding, failure analysis, and regression testing - Help productionize research work through pipelines, APIs, services, monitoring, versioning, and deployment workflows - Improve MLOps practices around experiment tracking, prompt/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-ready What success looks like by the end of the internship - At least one meaningful ML or LLM system is measurably improved in quality, reliability, or latency - Research work is backed by reproducible evaluation and monitoring rather than one-off experimentation - The path from experiment to production is cleaner, faster, and safer What we’re looking for - 3–4 years of relevant experience preferred through research labs, internships, startups, open-source work, or production ML systems - Strong software engineering ability and strong comfort writing production-quality code - Strong Python skills preferred - Experience with machine learning experimentation, evaluation, and debugging preferred - Experience with LLMs, retrieval systems, vector search, ranking, prompt/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 ideas into shippable systems - Strong communication and the ability to document tradeoffs, assumptions, and results Nice to have - Experience with PyTorch, Transformers, or modern ML tooling - Experience with vector databases, RAG systems, or evaluation harnesses - Experience with time-series forecasting, causal analysis, anomaly detection, or optimization systems - Experience with Docker, Kubernetes, cloud infrastructure, or batch/orchestration systems - Publications, benchmark work, or strong public repos/writeups Work authorization Applicants must be authorized to work in the United States for the full internship period and must be based in the U.S. during the internship. We are not able to provide employment visa sponsorship for this internship. We welcome applicants from all backgrounds and evaluate candidates based on technical depth, execution, communication, and fit for the role.

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 turning a strong product architecture into a production-ready system that is reliable, observable, scalable, and easy to ship. We are hiring a Software Engineer Intern (Platform & DevOps) for a paid 12-week summer internship from May through July 2026. This is a remote role based in the United States and is expected to be 40 hours per week. Compensation for this internship is $23/hour. In this role, you will help us scale the platform and strengthen the engineering foundation behind the product. This is a high-ownership internship for someone who enjoys backend systems, infrastructure, reliability, automation, and turning prototypes into production-grade software. What you’ll work on - Improve deployment, release, and environment workflows so the team can ship faster with fewer issues - Strengthen observability across services, jobs, and pipelines using logs, metrics, tracing, alerts, and dashboards - Improve the reliability and performance of ingestion jobs, scheduled metric computation, backend services, and platform tooling - Help harden infrastructure across cloud services, object storage, databases, secrets, permissions, and service configuration - Improve CI/CD, test automation, rollback readiness, and production debugging workflows - Work across the engineering stack to make the system more stable, production-ready, and easier to maintain What success looks like by the end of the internship - Core services and jobs are easier to deploy, monitor, and troubleshoot - Engineering workflows are faster and more reliable - The platform is meaningfully closer to production-grade readiness in reliability, performance, and operations What we’re looking for - 2+ years of hands-on software engineering experience preferred through internships, startups, research labs, open-source work, or production projects - Strong programming fundamentals and comfort working in backend systems - Experience with DevOps, platform engineering, or SRE-style work preferred - Experience with containers, CI/CD pipelines, infrastructure automation, and cloud services preferred - Experience with relational databases, object storage/data lakes, and scheduled or background jobs preferred - Comfort debugging production issues, improving developer workflows, and writing maintainable code - Strong written communication, ownership, and execution Nice to have - Experience with Azure, AWS, or GCP - Experience with Docker, Kubernetes, Terraform, GitHub Actions, or similar tooling - Experience with Postgres, data pipelines, or analytics/ML infrastructure - Familiarity with security, access control, and performance tuning Work authorization Applicants must be authorized to work in the United States for the full internship period and must be based in the U.S. during the internship. We are not able to provide employment visa sponsorship for this internship. We welcome applicants from all backgrounds and evaluate candidates based on skills, execution, communication, and fit for the role.