AI Systems Architect – Hardware × Cloud

AI Systems Architect – Hardware × Cloud

Open Culture Science | SF Bay Area (Hybrid)

Cell biology is where computing was in 1975. The tools that drive drug discovery and regenerative medicine are room-sized machines costing millions of dollars, operated manually by researchers working nights and weekends. Protocols disappear when the postdoc leaves. Data sits in local drives… unstructured, unlabelled, and unshareable.

Open Culture Science is changing that. We’ve built Habitat, a compact, AI-native hardware platform that automates the full cell culture workflow: stem cell expansion, 3D organoid formation, and drug assay.

We have 8 prototypes running in labs across the Bay Area, 20 active users, 5 peer-reviewed publications, and placements in the QB3 <> Bakar Bio Labs ecosystem. Our pre-seed closed oversubscribed by nearly 200%. We’re opening our seed round in H2 2026.

The science works. The hardware works. Now we’re building the intelligence layer.

We are hiring an AI Systems Architect to design and build the intelligence layer of the Open Culture platform – the architecture that connects Habitat devices (sensors, actuators, fluidic systems, imaging hardware) to NVIDIA GPU compute, cloud services, and AI models. You will turn a biology lab into a living software system.

This is a senior individual contributor role reporting directly to the co-founders. You will work alongside our Head of Analysis (a DARPA fellow with a background in AI agents), our CTO (PhD Bioengineering, expert in software, fabrication, and networking), and our Lead Stem Cell Biologist. You’ll be one of the first ten hires. Everything you build will matter.

This is a founding team role with equity compensation reflecting the stage and your impact.

This is a startup. The scope is wide on purpose. You will move across these areas based on what matters most, week to week.

The OC2 is the nervous system of the Habitat platform, connecting devices across labs, handling protocol execution and scheduling, routing biological signals to AI processing nodes, and piping structured data into models. You will architect this from the ground up.

• Design and build the IoT messaging architecture that connects disparate physical instruments (microscopes, MEA electrophysiology rigs, fluidic pumps, temperature controllers) into a unified, addressable system – building on our existing MQTT/AWS infrastructure and scaling it to production
• Build NVIDIA GPU compute pipelines that process raw sensor streams (image sequences, electrophysiology recordings, fluidic telemetry) in real time and route outputs to downstream AI models
• Design the biological data schema – structured, versioned, labeled, and shareable – that underpins our long-term vision of a verified cell culture data marketplace
• Connect the hardware control layer to the AI-native operating system of Habitat, so the device is not just automated but agentic: learning from runs, correcting errors mid-experiment, and flagging anomalies before they become failures

Digital twin of a cell culture: given hardware state, reagent properties, protocol parameters, and sensor data at time T, the model predicts biological state at time T N and uses that prediction to adapt the protocol in real time.

• Develop the feedback control architecture that enables real-time, closed-loop protocol adaptation based on live sensor data (imaging, electrophysiology, fluidics)
• Build and deploy on-device inference pipelines running on embedded NVIDIA hardware (not just cloud-based) for real-time decision making at the bench
• Develop protocol versioning and reproducibility infrastructure: every run produces a versioned, reproducible, shareable protocol that any Habitat user can download and run

• Work closely with the technical founders (CEO & CTO) to define and maintain the software interface with Habitat’s physical control systems (fluidics, valves, sensors, imaging)
• Architect the agent framework (MCP-based) that connects vision models, LLMs, robotics, and cloud services into a coherent, AI-native laboratory operating system
• Contribute to platform decisions that will define the product for years, more than implementing a spec, you are narrating it

• Strong experience architecting IoT or distributed sensor systems, connecting physical hardware to cloud infrastructure at scale
• Hands-on experience with NVIDIA GPU compute in a production context (Jetson, A100, H100, or equivalent); comfortable designing inference pipelines for real-time sensor data
• Demonstrated ability to build AI/ML pipelines end-to-end: data ingestion, model training or fine-tuning, deployment, monitoring
• Experience with cloud infrastructure: AWS (S3, IoT Core, Glacier, or equivalent), containerization, and orchestration (Docker, Kubernetes, or similar)
• Experience with MQTT or equivalent pub/sub messaging protocols for real-time device communication
• Strong software engineering fundamentals – you write clean, testable, documented code and think carefully about system design

• Experience with agent frameworks and MCP (Model Context Protocol), connecting LLMs and vision models to real-world tools, APIs, sensors, or robotics
• Background in robotics or mechatronics, understanding how physical actuation systems interface with software control layers
• Familiarity with biological or biomedical data, electrophysiology (MEA), microscopy image analysis, multi-omics, or microfluidics
• Experience with Nextflow, Dockstore, or similar workflow management tools
• Exposure to digital twin methodologies or model-based systems engineering
• Existing relationships with CROs (Charles River, PPD/Thermo Fisher, Labcorp), pharma (AstraZeneca, Merck, Genentech), or academic partners (Stanford, UCSC, Northwestern)

Beyond the technical skills, we are hiring for three qualities that define every great early-stage team member:

High autonomy. You take ownership. You don’t wait to be told – when something needs solving, you solve it and report back. You actively seek more responsibility, not less.

High openness. You stay current with fast-moving AI and hardware fields as a matter of course, not as a job requirement. You’re adaptable and genuinely excited by the unknown.

High conscientiousness. You understand the implications of your decisions. You think about the big picture. You can sensibly weigh risk against speed, and you communicate that reasoning clearly to the team.

The FDA Modernization Act 2.0 has removed the two-animal requirement for IND filings, putting human-relevant in vitro models at the center of preclinical drug development. The NIH has opened an $87M Standardised Organoid Modeling center. Charles River called 2025 “the year of alternative methods.” The preclinical CRO market is $8.62B, largely manual and underautomated.

We are early, well-positioned, and moving fast.