Engineering life itself: designing DNA, proteins, and microbes that make medicines, foods, fuels, and materials, often with robots and AI.
Top pay: highest pay, longest training
Synthetic Biologist: Designs and builds new biological parts and systems, like programming cells to make medicines or materials. Typical pay $125k.
Metabolic Engineer: Rewires the metabolism of microbes so they produce fuels, foods, or chemicals. Typical pay $125k.
AI Protein Design Scientist: Uses AI tools to invent brand-new proteins that don't exist in nature, for medicines and materials. Typical pay $145k.
Biofoundry Automation Engineer: Programs the robots that build and test thousands of engineered microbes at once. Typical pay $120k.
Industrial Enzyme Scientist: Designs enzymes, natural protein machines, that make laundry detergent work in cold water, turn corn into fuel and make medicines greener. Typical pay $112k.
Mid pay: solid pay, degree or apprenticeship
Biosecurity Specialist: Makes sure biotechnology isn't misused to create dangerous organisms. Typical pay $100k.
Synthetic Biology Research Associate: Lab scientist who builds and tests engineered DNA and cells for synthetic biology companies. Typical pay $72k.
Mycelium Materials Scientist: Grows mushroom roots (mycelium) into packaging, leather-like fabric, building panels and foam that can replace plastic. Typical pay $92k.
Community Biolab Director: Runs a public biology lab where anyone, from teens to retirees, can take classes and do real DNA and microbiology projects. Typical pay $55k.
Starter: fastest way in
Fermentation Technician: Runs the tanks where engineered microbes grow and make products. Typical pay $48k.
DNA Assembly Technician: Prepares and checks DNA samples in a synthetic biology or gene synthesis lab. Typical pay $48k.
Biofoundry Technician: Runs and maintains the lab robots that build and test engineered cells. Typical pay $52k.
Career Universe is an early version. Pay, training and AI-risk figures are estimates to start your own research, not verified facts. The research was written with AI research assistants, checked by validation scripts and spot-checked by hand. Check anything important with schools, employers and official sources. How the numbers work.