Prellis Biologics
Scientific Data Platform Architect — Antibody Discovery
Prellis Biologics, Berkeley, California, United States, 94709
About Prellis Biologics
At Prellis we integrate human biology with machine learning. We aim to revolutionize drug discovery by harnessing the power of the human immune system with tightly integrated machine learning to develop next‑generation antibody therapeutics with unprecedented speed, precision, and safety. We are committed to empowering our pharmaceutical partners with access to the most promising fully human body candidate rapidly identified from the human immune repertoire, enabling them to bring life‑changing treatments to patients faster than ever before. Prellis Biologics is a pre‑IPO biotech located in Berkeley, CA with a team‑oriented, inclusive, and family‑friendly culture. Our growing pipeline targets high unmet patient needs across therapeutics including metabolic, inflammation, and oncology disease. Prellis has raised funding from top investors, including Celesta, Khosla Ventures, SOSV, & Avidity Partners.
About The Role
You’ll architect and build hands‑on the end‑to‑end scientific data platform that powers antibody discovery and characterization. This includes a well‑structured PostgreSQL backbone on AWS, reliable ETL from lab systems (Benchling, PipeBio, instruments), and a scientist‑friendly app (Shiny or Python) with built‑in analytics and visualizations. You’ll design for FAIR data (Findable, Accessible, Interoperable, Reusable) and publish AI/ML‑ready datasets with clear lineage and versioning.
Platform architecture & data modeling (Postgres on AWS)
Own the canonical schemas (with selective JSONB), indexing/partitioning, materialized views, and stable entity IDs (samples, sequences, assays, runs).
Operate RDS/Aurora PostgreSQL, S3 for raw artifacts, and right‑sized IAM/VPC access; set guardrails for backups, recovery, and monitoring (CloudWatch).
FAIR by design & governance
Make data Findable (catalog/registry tables, searchable metadata), Accessible (role‑based access, documented APIs/exports), Interoperable (controlled vocabularies, standard formats such as CSV/Parquet, FASTA/VDJ, FCS/SPR), and Reusable (required metadata, units/QC flags, versioned tables).
Define and enforce data contracts, provenance, and lightweight review checkpoints.
Ingestion & transformation (ETL/ELT)
Build parsers/pipelines for instrument exports (CSV/TSV, FCS, ELISA/SPR/BLI), PipeBio repertoire/QC outputs, and Benchling entities via API/webhooks.
Add validation, unit normalization, schema migrations, and automated checks.
Analytics & visualization (data display layers)
Create curated analytic views (assay roll‑ups, QC dashboards, lineage), and implement interactive visuals (dose–response fits, sensograms, flow summaries, repertoire plots) with Plotly/Dash, Shiny, Spotfire, Streamlit, or similar.
Deliver drill‑downs, comparisons across runs/targets, and clean CSV/Excel exports.
Application layer
Build and maintain a small Shiny (R/Python) or Python app (FastAPI Dash/Plotly/Streamlit) that is role‑aware, searchable, and easy for scientists to use; deploy simply (EC2/ECS/Docker).
AI/ML interface
Publish feature‑ready Parquet/Arrow datasets (sequence features, developability metrics, assay labels like KD/EC50, clonotypes) with dataset versioning, timestamps, and lineage.
Provide reproducible extracts/snapshots for training, and ingest model predictions/scores back into Postgres and the UI.
Technical leadership
Set patterns and code standards, mentor contributors, review designs, and coordinate with Biology, Analytics, and QA/Compliance.
Keep cost/performance sane; evolve the roadmap as assays and throughput grow.
Immediate Projects
A clear Postgres schema with stable IDs, required metadata, and provenance supporting FAIR discovery.
Automated ETL for Benchling PipeBio instruments, with validation and unit normalization.
A usable app delivering interactive analytics & visualizations scientists rely on daily.
ML‑ready datasets with documented contracts; backups, monitoring, and a published data dictionary/metadata guide.
Minimum Qualifications
Bachelor’s degree in Computer Science or similar field.
7 years building data platforms or complex data products; expert SQL/PostgreSQL (schema design, optimization, migrations).
Strong Python or R for data engineering and app development (Pandas/SQLAlchemy or Shiny/Plotly/Streamlit).
Proven ETL experience from files/APIs and pragmatic scheduling (cron/Airflow/Prefect—keep it simple).
Practical AWS with Postgres on RDS/Aurora, S3 for storage, basic IAM/VPC, and CloudWatch for monitoring.
Hands‑on analytics & visualization for scientific datasets.
Working knowledge of FAIR principles and shaping AI/ML‑ready datasets (features, labels, versioned exports).
Nice to have
Benchling developer experience (entities, webhooks) and familiarity with PipeBio outputs.
Exposure to lab data types (FCS, BLI/SPR, ELISA, NGS summaries, PDB) and data integrity concepts (ALCOA , 21 CFR Part 11 basics).
Light containerization (Docker) and deploying a small app on EC2/ECS.
Experience round‑tripping model outputs to a database/UI; comfort with Jupyter/scikit‑learn/PyTorch.
What You Can Expect Of Us
As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well‑being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.
The expected annual salary range for this role in the U.S. is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.
Benefits
A competitive employee benefits package, including group medical, dental and vision coverage, life and disability insurance, flexible spending accounts, and a 401(k) plan.
Stock‑based long‑term incentives.
Bonus plan.
Holiday package including a 1‑week winter shutdown.
Flexible work models, including remote and hybrid working arrangements, where possible.
Application deadline
Prellis does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
Prellis Bio is an equal‑opportunity employer. All applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams—grounded in a wide range of expertise and life experiences—and work even harder to ensure those teams thrive in inclusive, growth‑oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process.
#J-18808-Ljbffr
At Prellis we integrate human biology with machine learning. We aim to revolutionize drug discovery by harnessing the power of the human immune system with tightly integrated machine learning to develop next‑generation antibody therapeutics with unprecedented speed, precision, and safety. We are committed to empowering our pharmaceutical partners with access to the most promising fully human body candidate rapidly identified from the human immune repertoire, enabling them to bring life‑changing treatments to patients faster than ever before. Prellis Biologics is a pre‑IPO biotech located in Berkeley, CA with a team‑oriented, inclusive, and family‑friendly culture. Our growing pipeline targets high unmet patient needs across therapeutics including metabolic, inflammation, and oncology disease. Prellis has raised funding from top investors, including Celesta, Khosla Ventures, SOSV, & Avidity Partners.
About The Role
You’ll architect and build hands‑on the end‑to‑end scientific data platform that powers antibody discovery and characterization. This includes a well‑structured PostgreSQL backbone on AWS, reliable ETL from lab systems (Benchling, PipeBio, instruments), and a scientist‑friendly app (Shiny or Python) with built‑in analytics and visualizations. You’ll design for FAIR data (Findable, Accessible, Interoperable, Reusable) and publish AI/ML‑ready datasets with clear lineage and versioning.
Platform architecture & data modeling (Postgres on AWS)
Own the canonical schemas (with selective JSONB), indexing/partitioning, materialized views, and stable entity IDs (samples, sequences, assays, runs).
Operate RDS/Aurora PostgreSQL, S3 for raw artifacts, and right‑sized IAM/VPC access; set guardrails for backups, recovery, and monitoring (CloudWatch).
FAIR by design & governance
Make data Findable (catalog/registry tables, searchable metadata), Accessible (role‑based access, documented APIs/exports), Interoperable (controlled vocabularies, standard formats such as CSV/Parquet, FASTA/VDJ, FCS/SPR), and Reusable (required metadata, units/QC flags, versioned tables).
Define and enforce data contracts, provenance, and lightweight review checkpoints.
Ingestion & transformation (ETL/ELT)
Build parsers/pipelines for instrument exports (CSV/TSV, FCS, ELISA/SPR/BLI), PipeBio repertoire/QC outputs, and Benchling entities via API/webhooks.
Add validation, unit normalization, schema migrations, and automated checks.
Analytics & visualization (data display layers)
Create curated analytic views (assay roll‑ups, QC dashboards, lineage), and implement interactive visuals (dose–response fits, sensograms, flow summaries, repertoire plots) with Plotly/Dash, Shiny, Spotfire, Streamlit, or similar.
Deliver drill‑downs, comparisons across runs/targets, and clean CSV/Excel exports.
Application layer
Build and maintain a small Shiny (R/Python) or Python app (FastAPI Dash/Plotly/Streamlit) that is role‑aware, searchable, and easy for scientists to use; deploy simply (EC2/ECS/Docker).
AI/ML interface
Publish feature‑ready Parquet/Arrow datasets (sequence features, developability metrics, assay labels like KD/EC50, clonotypes) with dataset versioning, timestamps, and lineage.
Provide reproducible extracts/snapshots for training, and ingest model predictions/scores back into Postgres and the UI.
Technical leadership
Set patterns and code standards, mentor contributors, review designs, and coordinate with Biology, Analytics, and QA/Compliance.
Keep cost/performance sane; evolve the roadmap as assays and throughput grow.
Immediate Projects
A clear Postgres schema with stable IDs, required metadata, and provenance supporting FAIR discovery.
Automated ETL for Benchling PipeBio instruments, with validation and unit normalization.
A usable app delivering interactive analytics & visualizations scientists rely on daily.
ML‑ready datasets with documented contracts; backups, monitoring, and a published data dictionary/metadata guide.
Minimum Qualifications
Bachelor’s degree in Computer Science or similar field.
7 years building data platforms or complex data products; expert SQL/PostgreSQL (schema design, optimization, migrations).
Strong Python or R for data engineering and app development (Pandas/SQLAlchemy or Shiny/Plotly/Streamlit).
Proven ETL experience from files/APIs and pragmatic scheduling (cron/Airflow/Prefect—keep it simple).
Practical AWS with Postgres on RDS/Aurora, S3 for storage, basic IAM/VPC, and CloudWatch for monitoring.
Hands‑on analytics & visualization for scientific datasets.
Working knowledge of FAIR principles and shaping AI/ML‑ready datasets (features, labels, versioned exports).
Nice to have
Benchling developer experience (entities, webhooks) and familiarity with PipeBio outputs.
Exposure to lab data types (FCS, BLI/SPR, ELISA, NGS summaries, PDB) and data integrity concepts (ALCOA , 21 CFR Part 11 basics).
Light containerization (Docker) and deploying a small app on EC2/ECS.
Experience round‑tripping model outputs to a database/UI; comfort with Jupyter/scikit‑learn/PyTorch.
What You Can Expect Of Us
As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well‑being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.
The expected annual salary range for this role in the U.S. is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.
Benefits
A competitive employee benefits package, including group medical, dental and vision coverage, life and disability insurance, flexible spending accounts, and a 401(k) plan.
Stock‑based long‑term incentives.
Bonus plan.
Holiday package including a 1‑week winter shutdown.
Flexible work models, including remote and hybrid working arrangements, where possible.
Application deadline
Prellis does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
Prellis Bio is an equal‑opportunity employer. All applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams—grounded in a wide range of expertise and life experiences—and work even harder to ensure those teams thrive in inclusive, growth‑oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process.
#J-18808-Ljbffr