Applied AI Engineer

Becoming Applied AI Engineer

Becoming is building Developmental Intelligence: AI for predicting how organisms change over time. Most experimental systems fail when metabolic demands become too high. We are building systems that don't — by tightly integrating hardware, biology, and software into platforms that operate continuously over long time horizons. Software is the connective tissue of this platform. It turns physical systems into controllable, observable, and ultimately predictable systems.

We are seeking an Applied AI Engineer to help build the software, infrastructure, and data systems that power our biological intelligence platform. This role sits at the intersection of software engineering, AI infrastructure, data engineering, and scientific computing. You will work closely with biologists, machine learning researchers, and automation engineers to create systems that transform experimental data into predictive models of living systems.

You will contribute across the stack—from laboratory data ingestion pipelines and cloud infrastructure to internal tools, model-serving systems, and user-facing applications. This role is ideal for engineers who enjoy building practical AI systems and are excited to work on technically challenging problems in biology.

• Software Engineering
  • Design and develop production-grade software systems
  • Build internal applications and scientific tooling
  • Develop APIs and backend services
  • Build and maintain web applications and dashboards
  • Create systems for experiment tracking and data visualization
  • Improve software reliability, testing, and deployment processes
• Data & AI Infrastructure
  • Build and maintain large-scale biological data pipelines
  • Design systems for ingestion, storage, transformation, and retrieval of multimodal biological data
  • Develop infrastructure supporting AI model training and evaluation
  • Optimize data movement between laboratory systems, cloud environments, and computational pipelines
  • Improve dataset quality, lineage, reproducibility, and governance
  • Support model serving and inference infrastructure
• Cloud & Platform Engineering
  • Design and maintain cloud infrastructure
  • Improve scalability, reliability, and observability of internal systems
  • Manage containerized and distributed workloads
  • Build deployment and CI/CD systems
  • Support high-performance computing and GPU infrastructure
  • Optimize cloud utilization and cost efficiency
• Security & IT
  • Improve organizational cybersecurity posture
  • Manage identity and access controls
  • Implement security monitoring and incident response processes
  • Support device management and endpoint security
  • Develop data security and compliance practices
  • Help establish security standards appropriate for sensitive biological and AI data
• Cross-Functional Collaboration
  • Work closely with scientists to understand data generation workflows
  • Partner with machine learning researchers to support model development
  • Collaborate with automation and hardware teams on laboratory integrations
  • Translate scientific requirements into scalable software systems

You are someone who:

• Operates with high agency — you see gaps across the stack and take ownership of fixing them
• Takes responsibility for end-to-end product outcomes, not just individual components
• Brings high energy to building robust, usable, real-world software
• Acts with high integrity — you care about correctness, reliability, and clarity
• Communicates directly and clearly, especially when tradeoffs or failures arise
• Is self-aware about your strengths and gaps, proactively fills them and open to feedback