Your Impact at LILA
You will build the back-end and data foundations of the systems that schedule work across our autonomous labs, model them as digital twins, and turn raw experimental output into trustworthy, queryable data. In Robotics, you'll own core services scientists, operators, and engineers rely on daily — the ones that decide how efficiently our robots run and how much our scientists trust their data.
What You'll Be Building
- Design scheduling systems: allocate experiments, instruments, and compute across autonomous labs, modeling long-running jobs as durable Temporal workflows.
- Model digital twins: stand up the simulation/digital-twin data layer so teams predict factory behavior before committing resources.
- Own technical data management: build the Flyte pipelines, S3 lakehouse, and PostgreSQL models that turn instrument output into governed, trustworthy data.
- Enable data science: ship batch and event-driven pipelines that put clean data in the hands of scientists and models.
- Deliver capacity planning: create customer-facing services that help teams forecast factory capacity.
- Operate for scale: run services on AWS EKS with Docker/ECR, Terraform, and GitHub Actions, reliable as the factory grows.
What You'll Need to Succeed
- 4–8 years of back-end engineering: production services in Python (FastAPI) at scale.
- Data engineering: pipelines on a workflow orchestrator such as Flyte, Temporal, or similar.
- Data modeling across stores: hands-on SQL (PostgreSQL), object storage (S3), and data lakehouse architectures.
- Cloud-native delivery: a major cloud with containers, infrastructure as code, and CI/CD — ideally AWS EKS (Docker/ECR), Terraform, and GitHub Actions.
- End-to-end ownership: taking services from design through delivery, reliability, and iteration.
- Clear collaboration: communicating technical tradeoffs plainly with engineers, scientists, and business partners.
Bonus Points For
- Systems engineering in regulated industries: systems-thinking rigor in safety- or quality-critical settings — aerospace, manufacturing, or healthcare/life sciences. Exposure to 21 CFR Part 11, ALCOA+, GAMP 5 / FDA CSA, or ISA-95 a plus.
- Domain exposure: robotics, lab automation, LIMS, or manufacturing/MES environments.
- Scheduling & optimization: job scheduling, resource allocation, or constraint/optimization problems.
- Data-science enablement: distributed compute (Ray), experiment tracking (Weights & Biases), or model serving.
- Event-driven messaging: NATS, MQTT, or similar pub/sub for high-volume telemetry.
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.
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.
Skills Required
- 4-8 years of back-end engineering experience building production services in Python (FastAPI) at scale.
- Data engineering experience building pipelines on workflow orchestrators such as Flyte or Temporal.
- Data modeling and hands-on SQL experience with PostgreSQL, object storage (S3), and data lakehouse architectures.
- Cloud-native delivery experience with containers and infrastructure as code; ideally AWS EKS (Docker/ECR), Terraform, and GitHub Actions.
- Proven end-to-end ownership of services from design through delivery, reliability, and iteration.
- Clear collaboration and communication of technical tradeoffs with engineers, scientists, and business partners.
What We Do
Lila is a technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method.
