Jobs at Lila Sciences

Owns strategic enterprise accounts across materials, chemicals, energy, and semiconductors. Responsibilities include developing multi-year account strategies, managing executive relationships, leading complex technical sales pursuits, negotiating large agreements, forecasting revenue, responding to technical RFIs and DDQs, and shaping commercial strategy. The role translates AI and autonomous laboratory capabilities into scientific and commercial value while partnering with product, science, software, robotics, legal, finance, and procurement teams.

Own a portfolio of top pharmaceutical accounts and drive revenue through multi-year account strategies, executive relationship development, complex enterprise pursuits, negotiations, renewals, and expansions. Partner with product, science, software, robotics, legal, finance, and procurement teams to close large agreements and scope high-value engagements. Maintain forecasts, lead technical RFI and DDQ responses, shape pricing and commercial strategy, and translate AI and autonomous-lab capabilities into credible scientific and business narratives.

Build and lead LILA’s supply chain demand-planning function, including order tracking, long-lead item management, demand signals, blanket-order sizing, inventory modeling, and delivery-capacity coordination. Partner with Procurement, Finance, Facilities, Lab Operations, suppliers, freight providers, and receiving teams to improve end-to-end visibility from order placement through installation and commissioning. Support ERP selection and implementation while resolving aging orders and escalating delivery risks.

Own lab equipment lifecycles, maintenance, calibration, installs, relocations, inventory, shipping, receiving, and vendor coordination. Support lab-space buildouts, safety and compliance activities, alarms, work-order systems, and operational process improvements. The role also onboards scientists to workflows, manages support tickets, mentors junior staff, and works hands-on across laboratory environments.

Build and lead enterprise platform infrastructure for multi-tenancy, identity, authorization, tenant isolation, metering, billing, audit logging, compliance, SDKs, and AI-native APIs. Architect scalable cloud-native systems for scientific data and workflows while coding hands-on in Python and influencing platform, infrastructure, security, AI/ML, and scientific application teams.

Develop and apply machine learning methods for protein engineering, including generative design, property prediction, candidate selection, active learning, and sequence or structure modeling. Collaborate with experimental scientists to translate biological objectives into computational problems, evaluate model generalization, interpret wet-lab results, and improve design-test-learn workflows for therapeutic biomolecules.

Develop BioML systems that encode and evaluate mechanistic biological reasoning. Analyze single-cell, perturbation, proteomics, imaging, and genetic data; design experimental campaigns; build outcome-verifiable benchmarks; and connect computational predictions with experimental decisions. The role requires rigorous evidence assessment, uncertainty interpretation, reproducible analysis, and collaboration with ML engineers, experimental scientists, and platform teams.

Design therapeutic proteins for active biologics programs, translating biological constraints into machine learning hypotheses. Build reasoning and orchestration systems for drug discovery, develop benchmarks and evaluation infrastructure, and improve reproducibility, throughput, and inference efficiency. Collaborate with domain scientists and AI researchers to convert expert judgment into ML objectives and scalable platform capabilities. The role requires strong software engineering, evaluation rigor, biological intuition, and expertise in protein sequence, structure, and function.

Leads computational protein engineering initiatives using generative and predictive machine learning to design therapeutic biomolecules. Owns workflows from design specification through wet-lab validation and active learning, develops sequence- and structure-based models, builds evaluation frameworks, and integrates protein design methods into autonomous science software. Collaborates with experimental scientists, AI researchers, and platform teams to interpret results and improve biomolecular design strategies.

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.

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.

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.

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.

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.