Jobs at Lila Sciences

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 go-to-market and launches for Lila's physical-science platform by crafting positioning, messaging, and collateral; enable sales and partners with technical content; gather market intelligence; represent Lila at events and with strategic partners to drive adoption in energy, chemicals, and advanced materials sectors.

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.

Lead DEL-based hit identification and complementary screening efforts, develop and optimize biochemical and biophysical assays, operationalize automation-enabled screening workflows, secure and design fit-for-purpose protein constructs, and drive hit validation and progression into hit-to-lead through cross-functional collaboration with chemistry, computational, and automation teams.

Develop and integrate ML models for RNA and DNA sequence design (UTRs, CDS, promoters/enhancers). Create generative, prediction, and active-learning methods; partner with experimental teams to design assays and validate sequences; investigate biological mechanisms and translate insights into improved models and platform capabilities; publish or share research externally.

Conducts high-throughput formulation and materials characterization, including rheological, thermal, chemical, tensile, peel, and shear testing. Prepares reagents, samples, and consumables; follows SOPs; records experimental data; troubleshoots workflow issues; and improves shift-based processes. The role involves wet-lab handling of polymers, coatings, adhesives, paints, and elastic materials while collaborating with scientists and engineers across life sciences and physical sciences.