Data Application Engineer, Foundry

Parallel Bio•San Francisco, CA

About The Position

At Parallel Bio, we are leveraging the human immune system to unlock safer, more effective drugs. We believe immunotherapies are the future of medicine, but their discovery is hindered by outdated models that fail to capture the complexity of the human immune system. Our platform overcomes these challenges by combining best-in-class human immune organoids with massive scale and advanced computational methods, including AI and machine learning. This allows us to generate unprecedented, population-scale insights into human health and disease. We can rapidly discover new drugs that we know will work in patients from the start and understand how they will perform across an entire population—something not possible with today's technology. This knowledge will allow us to engineer therapies that will work for as many people as possible, ensuring a safe and effective cure for everyone. We are a fast-paced, venture-backed company at a pivotal moment of growth. Join us on our journey as we create new tools to push the boundaries of what is possible. The Role This role is a strategic and operational extension of leadership within the Data & Infrastructure team. You can carry full context across the department's workstreams and act as a trusted proxy, making decisions, unblocking teams, and driving execution with minimal oversight. The right candidate has strong Foundry fluency, genuine curiosity about the science, and enough operational instinct to self-organize around the highest-value work without waiting to be told what to do. This is not a pure individual contributor role. It sits at the intersection of technical execution, project management, and strategic planning, and is designed for someone who can operate across those modes fluidly depending on what the department needs.

Requirements

2 to 6 years with Palantir Foundry, including ontology design, pipeline development, or application building
Proficiency with React, particularly for building Foundry applications and user-facing tools
Familiarity with cloud infrastructure (AWS) and modern data engineering practices
Startup or scale-up experience where scope is fluid and resourcefulness matters
Exposure to life sciences data (assay data, LIMS, genomics, or similar) is desirable; you should be comfortable following a science team discussion and translating it into data and infrastructure implications
High agency; you default to action with incomplete information
Clear communicator who can move between engineering architecture discussions and leadership briefings in the same afternoon
Builds systems, closes loops, creates structure where none exists
Genuinely curious about the science, not someone who treats scientific context as overhead

Nice To Haves

Prior "glue" role at a startup spanning technical and organizational domains
Familiarity with data governance, regulatory data requirements, or GxP-adjacent environments
Track record of earning trust with wet-lab or scientific teams as a non-scientist
Experience with distributed teams across US and European time zones

Responsibilities

(North of Ontology) Data Systems & Platform Infrastructure
Build and evolve the ontology (actions, objects, links) that represents our biological workflows in Foundry, and develop bespoke React applications that scientists and customers want to use
Drive the buildout of experimental data pipelines, storage architecture, and analytical tooling
Develop a working understanding of what data we have, what it is worth, and where the gaps are, then build workflows that unlock that value
Define and enforce data standards, schemas, and governance as dataset volume and complexity grow; partner with Automation and Science teams to ensure those standards reflect real experimental workflows, preventing data debt before it starts
Champion data-driven discovery across the company, raising Foundry literacy and helping scientists move from raw data to insight with increasing autonomy
Identify technical debt and infrastructure gaps; scope and prioritize remediation
Science Team Partnership
Develop a genuine, working-level understanding of science teams' priorities, experimental roadmaps, and active book of work by being in the room, not relying on secondhand summaries
Ensure Data & Infrastructure builds toward what science actually needs, not what looks logical from a systems perspective in isolation
Identify where data capture or pipeline gaps are creating friction for researchers and treat those with the same urgency as internal engineering priorities
Build enough trust with science leadership to anticipate needs and scope work proactively
Automation Team Coordination
Stay current on the automation team's roadmap so that data infrastructure remains compatible with the physical platform as it evolves
Where the two intersect (instrument integration, data ingestion, metadata standards), manage sequencing and dependencies sensibly without gatekeeping
Ensure the data layer keeps pace with expanding automation capabilities so increased experimental volume produces well-structured datasets, not cleanup backlogs
Keeping Things Moving
Self-organize around the department's highest-value work; seek out, sequence, and prioritize what needs doing rather than waiting for a task list
Own the operating rhythm: sprint planning, roadmap reviews, cross-functional syncs, dependency tracking
Surface risks and tradeoffs early on infrastructure delivery timelines
Translate technical constraints into business terms for BD, finance, and partnership discussions, where data infrastructure or security posture is relevant