The short version

We're replacing animal testing with a prediction layer for human drug response - and we need the person who's going to build the engine underneath it.

Parallel Bio runs "clinical trials in a dish" using patient-derived immune organoids that predict how drugs perform in humans with 87% concordance to clinical outcomes (vs. 3% for animal models). We're backed by $30M from Jeff Dean, Marc Benioff, Y Combinator, and AIX Ventures, and we're working with multiple Fortune 500 pharma partners. The FDA Modernization Act 3.0 just passed the Senate, and new FDA guidance provides a validation framework for our technology becoming the standard by 2035.

Now we need to scale the whole thing 10x.

The VP of Technology will own the full technology stack connecting our automated wet lab to a foundation model of human drug response. You'll lead data infrastructure, lab automation systems, MLOps and compute, and a growing engineering organization - reporting directly to the founders.

This is a 0→1 build. If you've been waiting for the role where you get to architect a system that doesn't exist yet, at the intersection of robotics, multi-modal biological data, and large-scale ML infrastructure - this is it.

Why this is hard

The data modalities don't exist in any textbook. The quality bar is set by FDA regulators, not product managers. The system you're building connects live human biology experiments to computational models that are being invented in parallel. There is no reference architecture.

The closest analog is probably the early infrastructure at an autonomous vehicle company or a semiconductor fab, except the thing you're modeling is the human immune system and every experiment produces data from a real patient's biology. If you've been looking for the role where the infrastructure decisions define what's scientifically possible, this is it.

What you'll own

Automation & Lab Systems. You'll oversee end-to-end lab orchestration: scheduling, instrument driver development, and integration of physical robotic workcells with digital infrastructure. You'll ensure tight coupling between automation systems and data pipelines so data flows from instrument to analysis in real time.

Data Infrastructure. You'll own the pipeline from raw instrument output to analysis-ready datasets and downstream model input. The data is high-dimensional and multi-modal - proteomics, transcriptomics, genomics, metabolomics, imaging, functional assays - all generated from the same patient-derived samples. You'll build the ingestion, QC, annotation, linkage, and storage layer, and ensure everything meets FDA-grade standards for regulatory submissions and partner deliverables.

MLOps & Compute. You'll define the MLOps strategy: distributed computing, cloud infrastructure, experiment tracking, model versioning, GPU/TPU provisioning for large-scale training. You'll bring judgment about when production-grade automation serves discovery and when it gets in the way.

AI/ML Oversight. You'll manage and set direction for the Head of AI, who owns model architecture and training. You need enough ML fluency to evaluate strategy, challenge assumptions, and make resourcing calls - but you're not doing the research yourself.

Team & Organization. You'll inherit a small team of ~4 across software and automation, assess talent, and scale to 20+ engineers over 18–24 months. You'll directly recruit team leads for physical infrastructure (platform biology + robotics), front-end, and back-end. You'll build a culture of strong technical judgment, accountability, and speed.

Strategy & Fundraising. As a member of the executive team, you'll shape company strategy, support business development, and articulate the technical vision to investors as we move toward Series B and beyond.

Why this role is different

Most VP of Technology roles are about maintaining and scaling something that already works. This one is about creating something that doesn't exist yet - the infrastructure connecting automated human biology experiments to a foundation model of drug response. The data modalities are unprecedented, the quality bar is FDA-grade, and there is no established playbook.

You'll be building the connective tissue between wet lab science, automation engineering, and AI research - translating biological requirements into engineering specs and infrastructure constraints into scientific tradeoffs. The closest analogs are the early infrastructure teams at autonomous vehicle companies, semiconductor fabs, or space tech - except the thing you're modeling is the human immune system.

What we're looking for

The profile. You've built and led engineering organizations at the intersection of physical systems and software. You've connected instruments, robots, or hardware to data pipelines and ML infrastructure - and you've done it in an environment where the playbook didn't exist yet. You reason from first principles, you have strong build-vs-buy instincts, and you've demonstrated commitment by staying long enough to see the hard problems through.

Experience

6+ years of professional experience; 2+ years leading technical teams at Director level or above (or equivalent CTO / Head of Engineering at a startup)
Deep data infrastructure expertise: pipelines, warehousing, QA, governance - ideally with scientific or multi-modal data (genomics, proteomics, imaging, or similar)
Hands-on experience bridging physical systems (lab instruments, robotics, automation) and software - not purely digital/cloud backgrounds
MLOps and compute infrastructure: distributed computing, cloud infra, experiment tracking, model versioning
Startup or early-stage experience: you've built teams and technical direction from scratch, not just optimized existing systems
At least one role with 3+ years of tenure - we value depth and commitment

Useful but not required

Experience with multi-modal biological data or lab orchestration / LIMS systems
Advanced degree in a quantitative discipline
Experience managing AI/ML research teams
Familiarity with data platforms like Palantir Foundry, Databricks, or Snowflake in a scientific context
Background in adjacent industries: autonomous vehicles, aerospace, semiconductor manufacturing, robotics, agricultural tech, or defense tech

About Parallel Bio

Parallel Bio was founded in 2021 to solve the most expensive failure mode in drug development: the inability to predict how drugs will perform in humans before clinical trials. Drug development today costs ~$3B per approved drug, takes 12.5 years, and fails 97% of the time. We're engineering the prediction layer that changes that equation.

Our platform uses patient-derived immune organoids - 3D models that replicate human immune system function - to run high-throughput experiments across hundreds of virtual patients. We've executed 10 pharma contracts with strong year-over-year revenue growth, and our data is already supporting an FDA filing for a partner's Phase 1 clinical trial.

We're not just building a better mousetrap. We're building the infrastructure for a world where drugs are designed and validated against human biology from day one.

Learn more at parallel.bio

风控数据分析师(J11304)

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