Applied AI Engineer

About Becoming

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.

The Role

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.

What You’ll Own

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

Who You Are

• 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
• Required
• BS, MS, or PhD in Computer Science, Engineering, Mathematics, Physics, or related field
• At least 1 year of industry experience
• Strong proficiency in modern frontend technologies (e.g. React, TypeScript, or similar)
• Strong backend experience (e.g. Python, Go, Rust, Node, or similar)
• Experience with cloud platforms (AWS, GCP, or Azure)
• Experience designing and maintaining APIs, services, and data models
• Comfort working with time-series data and stateful systems
• Strong understanding of databases and data architectures
• Familiarity with Linux environments and software deployment
• Thrives in startup fast pace and high intensity environments
• Competitive salary and meaningful equity depending on experience level
• Full benefits
• High-trust, high-ownership environment
• Rapid growth in scope and responsibility