Applied AI Engineer

Salary: $75,000 - 115,000 per year Requirements:

• Hold a BS, MS, or PhD in Computer Science, Engineering, Mathematics, Physics, or a related field
• Have at least one year of industry experience
• Demonstrate strong proficiency in modern frontend technologies such as React or TypeScript
• Have strong backend experience with technologies such as Python, Go, Rust, or Node.js
• Have experience with cloud platforms such as AWS, GCP, or Azure
• Have experience designing and maintaining APIs, services, and data models
• Be comfortable working with time-series data and stateful systems
• Have a strong understanding of databases and data architectures
• Be familiar with Linux environments and software deployment
• Be able to thrive in a fast-paced, high-intensity startup environment
• Take initiative, identify gaps across the stack, and own solutions end to end
• Focus on product outcomes, robust and usable software, and high standards of correctness and reliability
• Communicate clearly about tradeoffs and failures, remain open to feedback, and proactively address development areas

• Design and develop production-grade software systems, internal applications, and scientific tools
• Develop APIs, backend services, web applications, and dashboards
• Build experiment-tracking and data-visualization systems
• Improve software reliability, testing, and deployment processes
• Build and maintain large-scale pipelines for biological data ingestion, storage, transformation, and retrieval
• Develop infrastructure for AI model training, evaluation, serving, and inference
• Optimize data flows between laboratory systems, cloud environments, and computational pipelines
• Improve dataset quality, lineage, reproducibility, and governance
• Design and maintain cloud infrastructure, improving system scalability, reliability, and observability
• Manage containerized and distributed workloads and build deployment and CI/CD systems
• Support high-performance computing and GPU infrastructure, while optimizing cloud usage and costs
• Strengthen cybersecurity, manage identity and access controls, and implement security monitoring and incident-response processes
• Support device management and endpoint security, develop data-security and compliance practices, and establish standards for sensitive biological and AI data
• Work with scientists to understand data-generation workflows and with machine learning researchers to support model development
• Collaborate with automation and hardware teams on laboratory integrations
• Translate scientific requirements into scalable software systems

• AI
• AWS
• Azure
• Backend
• CI/CD
• Cloud
• Frontend
• GCP
• Hardware
• Support
• Linux
• Machine Learning
• Model Training
• Python
• React
• Rust
• Security
• TypeScript
• Web
• NodeJS
• DevOps
• Model Serving

More:

At Becoming, we are building Developmental Intelligence: AI that predicts how organisms change over time. We integrate hardware, biology, and software to create systems that operate continuously over long time horizons, with software making our physical platforms controllable, observable, and predictable. In this role, you will help build the software, infrastructure, and data systems behind our biological intelligence platform, working across software engineering, AI infrastructure, data engineering, and scientific computing with biologists, machine learning researchers, and automation engineers. Your work will span laboratory data pipelines, cloud infrastructure, internal tools, model-serving systems, and user-facing applications. We offer competitive salary and meaningful equity based on experience, full benefits, a high-trust, high-ownership environment, and opportunities for rapid growth in scope and responsibility.

last updated 42 week of 2026