The Opportunity

insitro is a physical AI company dedicated to unlocking causal human biology and accelerating the delivery of better medicines to patients. Our unique Virtual Human™ platform identifies novel, high-impact genetic intervention points, which our TherML™ platform translates into therapeutics—whether small molecules, biologics, or oligos. With multiple programs in metabolic disease and neuroscience advancing toward the clinic, and our first IND submission slated for the second half of this year, we are at a pivotal inflection point.

As a Senior/ Staff Software Engineer on our Imaging Software team, you will define and expand our data and ML infrastructure across the full data lifecycle — from data generation, high-throughput ML pipelines, to analysis results. You'll build the platform features that make novel in vitro screening modalities and ML-derived phenotypes integral to our discovery workflows, partnering with lab scientists, ML scientists, and our automation teams to turn research prototypes into validated screening workflows that reliably process terabytes of biological data per day. This is a chance to set the technical direction for how imaging, automation, machine learning, and agentic analysis converge in drug discovery.

Based in South San Francisco, this position reports directly to the Director of Imaging, Cellular Machine Learning and offers an in-person hybrid schedule of three days per week.

Responsibilities

Platform & Tooling

Platform Enablement: Partner with lab and ML scientists to design, develop, and scale the platform capabilities needed to run and interpret ML-powered high-content imaging screens.

User Tooling: Build and evolve robust tools and interactive interfaces for data exploration, quality assessment, and visualization so scientists can iterate quickly on experimental data.

Architectural Ownership: Own complex, end-to-end projects, making thoughtful architectural trade-offs, and delivering incrementally with long-term maintainability in mind.

Scalability & Data Integrity

Scalability: Scale and harden complex ML workflows, taking them from research prototypes to systems that reliably process terabytes of data per day.

Data Integrity: Set and uphold best-in-class practices for data integrity, lineage tracking, reproducibility, and observability across the entire data lifecycle.

Documentation: Write clear, exemplary technical specifications and documentation that others build on.

Cross-Functional Partnership

Scientific Translation: Work closely with lab scientists, ML scientists, and microscopy teams to translate complex experimental needs into clear, actionable technical plans and shipped software.

Mentorship: Raise the technical bar across the team by sharing knowledge and mentoring other engineers.

About You

Experience & Qualifications

Senior/ Staff experience: 6+ years of professional experience building and operating production-grade software and high-throughput data pipelines, primarily in Python.

ML Platform Depth (highly valued): You have designed, built, and maintained scientific computing pipelines, visualizations, and QC processes for large-scale biological datasets.

Core Stack: Hands-on experience with a Python-first ML stack (PyTorch/Lightning), distributed compute (Ray, Dask, Kubernetes, etc.), and workflow orchestration (Argo, Airflow, redun, etc.), deployed on cloud infrastructure at multi-terabyte scale.

End-to-End Delivery: A track record of owning complex systems from architecture through production operation.

Core Competencies

Cross-Functional Partnership: You thrive alongside scientists and excel at translating abstract research needs into practical, reliable, and scalable software.

Mission-Driven: You're motivated by enabling scientific breakthroughs through robust platform engineering.

Force Multiplier: You enjoy mentoring and leveling up the engineers around you.

Compensation & Benefits at insitro

Our target starting salary for successful US-based applicants for this role is $191,000 - $233,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.