Developmental Systems Lead (Fluidics/Perfusion) bei Becoming
Becoming · San Francisco, Vereinigte Staaten Von Amerika · On-site
- Optionales Büro in San Francisco
Description
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 — enabling sustained, controllable biological processes over long time horizons so that development, adaptation, and failure can be observed, perturbed, and predicted.
Physiology is central to this effort. Without stable, well-characterized physiological function, long-horizon prediction is impossible.
The Role
We are hiring a Developmental Systems Lead to own making externally sustained developmental systems run stably, repeatably, and predictably over days to weeks.
This role is for someone who thinks in systems, not endpoints — someone who understands how transport, metabolism, signaling, mechanics, and homeostasis interact over time. You will lead the design and operation of systems that integrate perfusion, physiology, sensing, actuation, and closed-loop control to sustain and perturb development in ways that generate meaningful, causal data.
This is a high-agency role. You will be expected to define measurements, interpret dynamics, and drive improvements in system-level function.
What You’ll Own
- End-to-end responsibility for externally sustained developmental systems operating over days to weeks
- Definition of what “success” means beyond “alive,” including how stability, drift, and failure are measured over time
- Identification, characterization, and mitigation of long-horizon failure modes (e.g., drift, inflammation, hemolysis, interface breakdown)
- Design and refinement of closed-loop control strategies for complex biological systems
- Integration of perfusion hardware, sensors, and control software into durable platforms
- Decisions about when systems are sufficiently stable to perturb, compare, and learn from
- Close collaboration with developmental biology, hardware, and ML / modeling teams
- Building systems that improve with iteration, not systems that require constant manual expert intervention
Who You Are
You are someone who:
- Operates with high agency — you identify what needs to be measured and why
- Takes ownership of outcomes, not just data collection
- Brings high energy to complex, dynamic biological systems
- Acts with high integrity — you are honest about uncertainty, limits, and tradeoffs
- Communicates directly and clearly, especially when systems are not behaving as expected
- Is self-aware about your strengths and gaps, proactively fills them and open to feedback
- Thinks like a systems integrator, not a siloed specialist
Is comfortable working where physiology, engineering, and modeling intersect
Requirements
Required
- Deep, hands-on experience with long-running biological or physiological systems (days+, not hours)
- PhD or equivalent experience in physiology, engineering, or a related field
- Experience with several of the following:
- Perfusion systems involving blood or blood analogs
- Hemodynamics, flow, pressure, and shear — and their biological consequences
- Inflammation, coagulation, hemolysis, or endothelial failure in sustained systems
- Closed-loop control of noisy, drifting systems
- Real-time instrumentation and sensing in biological environments
- Diagnosing failure modes that emerge gradually, not catastrophically
- Ability to work fluently with developmental biologists and/or physiologists
- At least 1 year of industry or applied systems experience
Strong Signals
- Background in perfusion, ECMO, organ support, or normothermic preservation
- Physiology-driven systems engineering experience
- Experience with complex robotics or control systems operating in irreversible environments
- History of systems that failed — and improved because of it
Benefits
- Competitive salary and meaningful equity
- Full benefits
- High-trust, high-ownership environment
- Rapid growth in scope and responsibility