Jobs at Lila Sciences

Design and fabricate prototype devices and test structures for novel materials research. Develop and troubleshoot thin-film deposition recipes and cleanroom processes, including lithography, etching, and metrology. Characterize films and devices using profilometry, ellipsometry, SEM, and electrical probing. Integrate fabrication into automated, high-throughput workflows and collaborate with materials scientists, device physicists, systems engineers, and machine learning scientists to execute rigorous fabrication campaigns.

Develop and validate XRF, SEM-EDS, and WDS characterization methods, calibrations, standards, and quality-control procedures. Perform measurements, interpret spectra, troubleshoot instruments and samples, and investigate anomalous data. Build analyzer software, spectral processing, automated ingestion, metadata, provenance, and machine-readable quality workflows in collaboration with AI, software, hardware, and automation teams. Maintain instrument readiness, documentation, SOPs, and support expansion to new analytical detectors.

Leads electron diffraction and electron microscopy characterization workflows for materials discovery. Responsibilities include sample preparation, measurements, crystal structure determination, microstructure analysis, method validation, quality control, troubleshooting, and reproducibility standards. The role also develops structured, traceable, machine-readable diffraction outputs and collaborates with ML, software, automation, and experimental teams to build scalable analyzer pipelines and automated characterization workflows.

Characterize thin-film materials, passive photonic structures, and electro-optic modulators at chip and wafer level. Design, calibrate, and debug optical and optoelectronic measurement setups; develop novel measurement protocols, quality controls, and wafer-level testing methods; analyze material, device, process, and uncertainty data; and communicate findings to materials, design, fabrication, simulation, ML, and automation teams.

Design end-to-end workflows for Lila’s scientific software platform, translating complex scientific, operational, and AI-native capabilities into intuitive experiences. Partner with product, engineering, science, and operations teams; conduct research and usability testing; prototype AI interactions; and contribute scalable design patterns and systems across the Lila App.

Own OT security engineering for laboratory instruments, automation, and building systems. Translate OT security architecture into controls (segmentation, identity, PAM, PKI), build detection and response, validate vendor integrations, run vulnerability and lifecycle management, and mentor engineers while partnering with SOC, OT engineering, and lab operations.

Build and improve applied AI models for scientific customer workflows. Responsibilities include model post-training, evaluation loop development, experiment design, debugging model behavior, and integrating AI capabilities into production software. The role collaborates with AI researchers and software engineers to adapt models, create reusable tooling, and translate customer feedback into model improvements.

The Machine Learning Engineer will design and implement ML pipelines and productionize models, collaborating with scientists and engineers on scalable systems.

Develops reproducible Python-based data analyses and bioinformatics pipelines for life sciences experiments. Translates wet-lab workflows and scientific questions into validated analytical requirements, integrates workflows with data lake and AI systems, and builds reusable analysis modules. Collaborates with scientists, automation specialists, software engineers, and ML engineers while identifying data, LIMS, and workflow gaps and documenting methods, assumptions, and results.

Lead end-to-end multimedia production across video, photography, graphics, animation, and scientific storytelling. Partner with scientists, engineers, product, sales, recruiting, communications, and facilities teams to translate complex concepts into compelling visual content. Manage production timelines, reviews, asset delivery, metadata, versioning, archiving, and reusable templates. Develop visual storytelling strategies and use AI-powered tools to increase production speed while maintaining quality.

Lead an 8–10-person engineering team building LILA’s AI-native application platform, including chat, agents, artifacts, and integrations with laboratory workflows and ML pipelines. Define technical strategy, oversee end-to-end product delivery, ensure reliability and security, collaborate with scientists and cross-functional partners, mentor engineers, and represent the company’s applied-AI work externally.

Partner with the CFO to prioritize and advance high-impact finance, people, operations, and strategy work. Shape operating cadence, prepare executive materials, track CFO-owned projects to closure, coordinate cross-functional stakeholders, resolve stalled work, and draft decision notes and executive communications to maintain momentum and clarity across the CFO portfolio.

Design and build high-performance UI and APIs, manage database architecture, optimize systems for reliability and performance, and collaborate cross-functionally in a scientific context.

Design and operate GPU/accelerator infrastructure and inference platforms for large ML models. Build Kubernetes-based scheduling, multi-tenant GPU sharing, autoscaling, model serving, IaC (Terraform/Helm) for EKS, CI/CD for model artifacts, observability for latency and throughput, and cost/capacity optimization across heterogeneous accelerators.

Use first-principles and atomistic simulations plus scientific ML and agentic AI to accelerate materials discovery and optimization. Perform electronic-structure and phonon calculations, connect simulation to experiment, build predictive models, generate actionable hypotheses, and collaborate with ML, software, and experimental teams to deploy autonomous discovery workflows.

Conduct computational research applying first-principles modeling, atomistic simulations, AI, and scientific machine learning to discover and optimize organic electronics materials. Investigate charge transport, excited-state behavior, morphology-property relationships, and device performance. Build predictive models, connect simulations with experiments, generate materials hypotheses, and collaborate with AI, software, computational, and experimental teams to develop autonomous scientific discovery workflows.

Design, develop, and deploy scalable laboratory automation and workcells. Partner with scientists, hardware, controls, and software teams to translate experimental goals into robust automated workflows, implement machine vision and sensor-based detection, perform FMEAs, troubleshoot and optimize systems, and lead technical teams to ensure safety, reproducibility, and regulatory alignment.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

Lead go-to-market and messaging for Lila's life science product portfolio (RNA/DNA design, medicinal chemistry). Define positioning, launch plans, and evidence strategies; create technical content and case studies; enable sales and customer success; and align product, commercial, and brand teams to drive adoption and measurable business impact.

Coordinate rollout and administration of lifecycle systems (PLM/QMS), support deployment and commissioning workflows, maintain operational documentation and asset lifecycle visibility, and drive cross‑functional execution for AISF platform deployments and the Blueprint systems program.