Applied AI Engineer
About BecomingBecoming 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 RoleWe 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 OwnSoftware 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 systemsWho You AreYou are someone who:Operates with high agency — you see gaps across the stack and take ownership of fixing themTakes responsibility for end-to-end product outcomes, not just individual componentsBrings high energy to building robust, usable, real-world softwareActs with high integrity — you care about correctness, reliability, and clarityCommunicates directly and clearly, especially when tradeoffs or failures ariseIs self-aware about your strengths and gaps, proactively fills them and open to feedbackRequirementsRequiredBS, 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 modelsComfort working with time-series data and stateful systemsStrong understanding of databases and data architectures Familiarity with Linux environments and software deploymentThrives in startup fast pace and high intensity environmentsBenefitsCompetitive salary and meaningful equity depending on experience levelFull benefitsHigh-trust, high-ownership environmentRapid growth in scope and responsibility