What You’ll Do

You’ll focus on building Python-based internal tools, scripts, and services that support our AI/ML and Software Engineering teams. Your work will enable data pipelines, internal infrastructure, experimentation, deployment workflows, and operational reliability across cloud and edge environments.

This role is ideal for a junior–intermediate engineer who is comfortable working close to infrastructure and data, enjoys automating repetitive tasks, and wants to grow their skills in cloud systems, ML-adjacent tooling, and production engineering.

You’ll collaborate closely with ML engineers, deployment engineers, and senior software engineers to improve developer velocity and system robustness.

Key Responsibilities

• Build and maintain Python-based internal tools and scripts used by AI/ML and software teams
• Develop utilities for data ingestion, transformation, validation, and pipeline automation
• Support internal infrastructure workflows (experiments, deployments, monitoring, backups, etc.)
• Write scripts and lightweight services to interact with cloud resources (e.g., S3, EC2, IAM)
• Help automate and improve internal development and deployment processes
• Work with Linux-based systems for development, testing, and production support
• Assist with debugging issues across data pipelines, internal services, and infrastructure
• Collaborate with senior engineers to improve reliability, scalability, and observability
• Contribute to internal documentation, tooling standards, and best practices

What We’re Looking For

Must-Haves

• Degree in Computer Science, Software Engineering, or equivalent practical experience
• Strong working knowledge of Python (scripts, modules, basic services)
• Comfort working in Linux environments (CLI, processes, filesystems)
• Experience or familiarity with cloud services, especially:
• AWS S3, EC2 (or equivalent services on other cloud platforms)
• Understanding of basic software engineering fundamentals:
• Version control (Git)
• Debugging and logging
• Writing readable, maintainable code
• Ability to work collaboratively with ML and software teams
• Interest in building internal tools and infrastructure (not just user-facing products)

Nice-to-Haves

• Exposure to data pipelines, ETL processes, or ML workflows
• Familiarity with Docker or basic container concepts
• Experience working with APIs, SDKs, or cloud CLIs
• Some exposure to CI/CD, automation, or DevOps practices
• Experience supporting or working alongside ML/AI systems
• Startup or fast-paced engineering environment experience

Why Join us?

• Work closely with AI/ML engineers on real production systems
• Build tooling that directly enables cutting-edge AI deployments
• Gain hands-on experience with cloud infrastructure and data systems
• Learn from senior engineers and researchers in a fast-growing startup
• Make a real impact behind the scenes — your tools will be used every day