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# Software Engineer (LATAM)

- **Company:** Recruiting from Scratch
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $60,000.0 - $90,000.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Big Data, Cloud Computing, Cloud Engineering, Data Files, Elasticsearch, Github, Desktop Environments, Python (Programming Language), RabbitMQ, Redis, Next.js, Software Engineering, TypeScript, Web Applications, Data Processing, Scripting, Backend, Kubernetes, Information Technology, Apache Kafka, Front End Software Development, Data Pipelines, Serverless Computing
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/software-engineer-latam-san-francisco-ca--43878853-1012-438e-87f2-735f60de2792

## About the Role

Software Engineering, Funding, Data Sets, Artificial Intelligence (AI), Startup, Desktop Environments, Data Collection, Develop and Maintain Customers, Python Programming/Scripting Language, JavaScript, * 4-10 years of professional full-stack software engineering experience.

 * Strong production experience with JavaScript/TypeScript and Python.
 * Strong experience with Next.js, ideally building complex user-facing web applications.
 * Full-stack engineer with particularly strong frontend/product engineering ability.
 * Experience building and scaling complex data-capture workflows.
 * Experience designing or optimizing high-throughput data pipelines.
 * Background at a reputable, rapidly growing startup rather than exclusively large-company environments.
 * Proven ability to own meaningful projects from architecture through production.
 * Experience with serverless or cloud-native architecture on AWS or GCP.
 * Hands-on experience with technologies such as Redis, Elasticsearch, Kafka, or RabbitMQ.
 * Strong English communication skills and comfortable collaborating with U.S.-based teams.
 * Comfortable inferring requirements and making decisions without highly detailed specifications.
 * Stable employment history with enough tenure to demonstrate meaningful project ownership.
 * Based in Latin America and able to maintain substantial overlap with U.S. working hours.

Preferred

 * Previous experience at an early-stage, VC-backed or YC-backed startup.
 * Founding engineer or early engineering team experience.
 * Experience working directly with founders on both product and technical strategy.
 * Strong GitHub profile demonstrating consistent hands-on engineering activity.
 * Experience building infrastructure for AI data, model training, evaluations, or agentic workflows.
 * Experience supporting high-throughput or data-intensive applications.
 * Familiarity with Kubernetes.
 * Experience working for a U.S.-based company or within a U.S.-centric engineering culture.
 * Computer Science degree from a strong technical university.
 * Experience working across both web applications and Python-based desktop or backend systems.
 * Strong product instincts in addition to technical execution.

## Description

* Build and scale complex data-capture workflows across web and desktop environments.
 * Develop customer-facing applications using Next.js, JavaScript/TypeScript, and Python.
 * Own projects end-to-end, from initial architecture and product decisions through implementation and production.
 * Architect and optimize high-throughput data pipelines handling significant workloads.
 * Build reliable infrastructure for capturing and processing data used in advanced AI and agentic workflows.
 * Work across frontend and backend systems, with a particular emphasis on building strong user-facing product experiences.
 * Design systems using technologies such as Redis, Elasticsearch, Kafka or RabbitMQ, and cloud infrastructure.
 * Build and operate serverless and cloud-native systems across AWS or GCP.
 * Collaborate directly with the founding team to define product strategy and determine what should be built.
 * Move quickly from ambiguous requirements to working production software.
 * Establish engineering patterns and best practices as the team grows.
 * Use modern AI development workflows to accelerate engineering and product iteration.
 * Take on increasing technical and organizational ownership as the engineering team expands.