Six Figure Work

Staff Engineer, Enterprise Externalization

Lila Sciences

Company websitelila.ai (Lila Sciences, opens in new tab)

Base salary $192K  to $288K

Employer-published annual base salary, USD. Advertised base range in United States

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  • LocationUnited States
  • WorkplaceWorkplace not stated
  • Status at last checkSource accepting applications when checked
  • Last checked (2 h ago) · observed since

About this job

Your Impact at LILA

Lila Sciences is building the infrastructure for autonomous science — AI systems that design experiments, run them in physical labs, and close the loop across life sciences, chemistry, and materials science. Our platform enables this to operate reliably at scale for external customers who require security, auditability, and operational rigor.

We’re hiring a Staff Engineer, Enterprise Platform to build the foundational systems that make Lila ready for the world’s largest pharmaceutical and research organizations.

Today we have the beginnings of multi-tenancy, identity and granular permissions and scopes, audit infrastructure, usage and billing.  Enterprise customers require significantly more. You will drive core enterprise capabilities to production: multi-tenancy and tenant isolation, enterprise identity and permissions to support the full lifecycle of the Lila Platform, metering and billing infrastructure, audit logging, etc. You will push the frontier — building agentic AI-powered systems for self-service tenant management, intelligent customer observability, automated compliance workflows, Lila SDK, MCP, and AI-native external APIs that let customers and third-party agents interact programmatically with Lila's platform.

This is a deeply technical senior individual contributor role focused on defining and building correct, secure, and scalable platform infrastructure. This is a high-autonomy, high-impact role. We move fast, ship weekly, and expect engineers who thrive in ambiguity not ones who wait for requirements to be handed to them. You will own outcomes.

What You'll Be Building

  • Enterprise infrastructure is foundational to making autonomous science deployable in the real world. The systems built in this role will determine how large organizations safely adopt, govern, and scale AI-driven scientific workflows across teams, data domains, and regulatory environments.

You’ll help define the platform primitives that make that possible.

  • Design and evolve the core identity systems powering Lila’s enterprise platform — organizations, groups, roles, SCIM provisioning, seat lifecycle management, and fine-grained authorization across experiments, datasets, models, and scientific workflows. You’ll define how tenant isolation, policy evaluation, and access governance work across the platform.
  • Design tenant-aware schemas, metadata systems, and platform primitives that support highly heterogeneous scientific data across life sciences and materials science workloads. Drive architectural decisions around scalability, reliability, retention, and operational correctness.
  • Architect systems for tenant provisioning, usage metering, billing, data migrations, compliance workflows, and operational automation. Push toward self-service and agentic operational workflows that reduce manual intervention while maintaining strong safety guarantees.
  • Operate as a senior technical leader across platform engineering, infrastructure, security, AI/ML, and scientific application teams. This is a hands-on Principal Engineer role with broad architectural influence, high autonomy, and direct ownership of critical platform systems.

What You'll Need to Succeed

  • 8+ years of software development experience, with a focus on Enterprise SaaS product development
  • Deep expertise in enterprise SaaS platform systems: authentication/authorization (OAuth 2.0, OIDC, SAML), multi-tenancy, RBAC, billing/metering, configurations, SDK and APIs
  • Experience with modern cloud-native architectures (Kubernetes, Terraform, AWS/GCP) and event-driven systems, familiarity with infrastructure-as-code and containerized deployments
  • Track record building enterprise platforms in high-growth environments where speed-to-value mattered as much as long-term architecture
  • Strong cross-functional communication skills and the ability to influence architecture across teams.
  • Hands-on coding in Python and modern backend frameworks (FastAPI, async Python)
  • BS, MS, or Ph.D. in Computer Science or a related field of study

Bonus Points For

  • You have practical experience with agentic systems and workflow automation where models take actions with real operational consequences in regulated environments.
  • You’ve worked on platforms serving scientific or laboratory data such as LIMS, ELN, bioinformatics, or related systems, and understand the provenance and traceability requirements associated with scientific workflows.
  • You’ve built self-service enterprise tooling for non-technical administrators and have thought carefully about the UX and API design required to make those systems usable.
  • Experience with compliance frameworks: SOC 2, HIPAA, GxP, FedRAMP, or ALCOA+
  • You’ve operated multi-tenant SaaS systems at significant scale with strict isolation, reliability, and compliance requirements.

Compensation

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range

$192,000—$288,000 USD

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

Source: Lila Sciences on Greenhouse · View original posting (opens in new tab)

Pay in context

This role's base floor ($192,000) is above 65% of the 4,313 Software Engineering roles in California listed on this site; 27 others list the same floor. Their median floor is $176,100; the middle half runs from $149,100 to $203,000.

Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 6, 2026.

This range next to 6 other live Lila Sciences roles with the nearest advertised base floor
Chart as a table
This range next to 6 other live Lila Sciences roles with the nearest advertised base floor
RolePublished base
Staff Engineer, Enterprise Externalization (this listing)$192K - $288K
Technical Program Manager, AI Data$192K - $272K
Staff Forward Deployed Engineer, Physical Sciences (Level Flexible)$192K - $256K
Staff Forward Deployed Engineer, Life Sciences$192K - $256K
Senior Manager, Scientific System of Record$192K - $256K
Staff Engineer, Data Platform$192K - $288K
Director, Materials AISF Program Lead$197,600 - $284,667

Same $100K to $400K scale as every card. All Lila Sciences roles · How to read the bar

Observed changes

  1. First observed on the employer's board
  2. Began meeting the published-salary criteria
  3. Advertised base range changed
Observed since
Employer-stated posting date
2026-09-21
Where this pay comes from

Location as the employer wrote it: San Francisco, CA USA

USD 192,000 – 288,000 · interval unstated · pay_input_range · Expected Base Salary Range · base-labelled (R1: Base Salary)

Method
Structured pay field published by the employer
Checked
Rule set
us-base-v2

The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.

What we verified

  • The pay figures come from the employer's own listing, quoted exactly above.
  • The pay is an annual base salary in US dollars with a minimum of at least $100,000.
  • The role was publicly listed on the employer's job board when last checked.

We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.