Software Engineer, Data Infrastructure & Acquisition

Software Engineer, Data Infrastructure & Acquisition

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineer, Data Infrastructure & Acquisition based in South Africa.

This role sits at the intersection of software engineering, data infrastructure, and applied AI, focusing on building and scaling the systems that power large-scale dataset acquisition for next-generation machine learning models. You will work in a fully distributed environment alongside engineers, researchers, and product leaders to design robust ingestion pipelines capable of handling massive, high-quality audio and text datasets. The work directly impacts how data is collected, processed, and transformed into training-ready assets that fuel AI innovation. You'll contribute to improving the cost, scale, and efficiency of data systems while helping define the roadmap for dataset development. The environment is fast-moving, highly collaborative, and deeply technical, with strong ownership and autonomy. This is a chance to shape foundational infrastructure used by millions of users globally.

Accountabilities

You will be responsible for building, maintaining, and scaling large-scale data ingestion and acquisition systems that support AI model training and product development. You will design and extend cloud-based infrastructure, optimize data pipelines, and ensure efficient processing of high-volume datasets across distributed systems. You will collaborate closely with AI scientists and engineering teams to improve data quality, reduce cost, and increase throughput for training workflows. You will also identify and integrate new external data sources, including audio and web-based datasets, into production pipelines. Additionally, you will help define dataset strategy and contribute to architectural decisions that support long-term scalability and reliability of infrastructure systems.

• Build and maintain scalable data ingestion and processing pipelines
• Extend cloud infrastructure (GCP) using Infrastructure-as-Code tools
• Identify and integrate new data sources into acquisition systems
• Collaborate with research and AI teams to improve dataset quality and efficiency
• Optimize systems for cost, throughput, and reliability at scale
• Contribute to architecture and roadmap decisions for data infrastructure

Requirements

The ideal candidate brings strong software engineering experience with a focus on distributed systems, data infrastructure, or backend engineering in production environments. You should have hands-on experience with Python and Linux-based development workflows, along with strong familiarity with cloud platforms such as GCP and infrastructure-as-code tools like Terraform. Experience with Docker, large-scale data pipelines, or web crawling systems is highly valuable. You are comfortable working in fast-paced, ambiguous environments and can manage multiple priorities effectively. Strong communication skills and the ability to collaborate across technical and research-driven teams are essential. A background in Computer Science or a related technical field is expected, along with a proven ability to build reliable and scalable systems.

• 5+ years of software engineering experience
• Strong proficiency in Python and Linux environments (bash scripting)
• Experience with GCP and Infrastructure-as-Code (Terraform preferred)
• Hands-on experience with Docker and cloud-native development
• Exposure to large-scale data pipelines or web crawling systems (preferred)
• Strong problem-solving and system design skills
• Excellent communication and cross-functional collaboration abilities
• Degree in Computer Science or related technical field (BS/MS/PhD)

Benefits

• Competitive base salary with bonus and equity opportunities
• Fully remote, distributed-first work environment
• High-impact role working on AI systems used at global scale
• Opportunity to shape foundational data infrastructure for ML models
• Collaborative, engineering-driven culture with strong autonomy
• Access to cutting-edge AI and data engineering technologies
• Fast-paced environment with ownership over meaningful technical problems
• Work on a product that improves accessibility and learning experiences worldwide