Jobs at Lila Sciences

Leads the engineering team responsible for LIMS user interfaces, applications, APIs, and data architecture supporting scientific workflows. Sets technical direction, shapes the roadmap, hires and develops engineers, and partners with scientific, product, ML, data, automation, and platform teams. Ensures systems provide reliable, auditable, traceable, and reproducible records of experiments, samples, protocols, results, and laboratory operations while integrating with automation and platform infrastructure.

Owns security engineering for AI and ML infrastructure, including training and inference pipelines, model registries, MCP servers, and agent runtimes. Designs security controls, conducts threat modeling and adversarial assessments, maintains compliance, and drives vulnerability remediation. Partners with AI Platform, Research, ML Operations, Science, and AI Safety teams to embed security across data, models, pipelines, and agentic systems.

Own end-to-end technical program management across multiple scientific software platforms, aligning requirements, dependencies, timelines, risks, and stakeholders. Partner with engineering leadership to scale processes, agile practices, release gates, intake workflows, Jira hygiene, backlog management, and operational dashboards. The role spans engineering teams and scientific domains including life sciences, formulations, and chemistry, requiring strong technical fluency in APIs, data models, schemas, and integrations.

Lead the inference engineering organization and own Lila’s model-serving platform end to end. Define APIs, serving architecture, batching, routing, deployment, reliability practices, and performance objectives for production LLM systems. Partner with ML, research, product, and executive teams to productionize models, establish evaluation and observability systems, and manage latency, throughput, quality, reliability, and cost. Hire, mentor, and retain engineering talent while setting the serving roadmap and technical strategy.

Own the end-to-end enterprise search and retrieval platform for scientific AI workloads. Build RAG over literature, patents, protocols, and experimental records; develop a scientific knowledge graph; and combine graph, vector, and lexical retrieval into cited, traceable answers. Lead ingestion, document extraction, agentic search, retrieval evaluation, tenancy, permissions, and security. Provide architectural leadership across infrastructure, AI/ML, platform engineering, and scientific teams while coding in Python and deploying cloud-native systems.

Lead full-cycle recruiting for AI/ML research and engineering roles. Develop sourcing strategies, build relationships with researchers and applied scientists, partner with technical leaders on hiring plans, manage interview processes and offers, strengthen employer branding, and maintain recruiting metrics and pipeline rigor. The role requires deep familiarity with AI/ML research talent markets, passive-candidate sourcing, technical evaluation, stakeholder management, and strategic talent planning.

Develop automated safety evaluations, red-teaming methods, reproducible experiments, and monitoring tools for scientific AI models, agents, and tool-mediated workflows. Investigate failure modes, assess evaluator quality, collaborate with domain experts and mitigation engineers, and translate findings into improved safeguards. The role may follow a Research Scientist or Research Engineer path, emphasizing either empirical research and experimental design or reliable research software and evaluation infrastructure.

Own end-to-end requisition-to-purchase-order execution, including order accuracy, queue prioritization, catalog coverage, supplier onboarding, vendor master management, and issue resolution. Partner with research and operating teams, suppliers, and FP&A on purchasing, compliance, budgets, coding, and accruals. The role supports scientific materials, equipment, and research services in a fast-paced AI-driven R&D organization.

Own the architecture, roadmap, security, and operation of Lila’s global multi-site network spanning corporate, laboratory, and OT environments. Design SD-WAN/SASE, firewalls, segmentation, remote access, monitoring, and IT/OT convergence. Lead migrations, incident escalations, vendor and MSP management, technical standards, runbooks, and network engineering capability development. Mentor engineers and advise leadership on reliability, security, cost, and delivery tradeoffs.

Own the architecture and roadmap for enterprise endpoint management across macOS, Windows, Linux, mobile, and research environments. Establish configuration-as-code, zero-touch provisioning, security integration, vulnerability management, observability, staged rollouts, and automated remediation. Build tooling and automation, support complex endpoint issues, mentor engineers, and lead initiatives across IT, Security, Research Computing, and Lab Operations while improving device security and employee productivity.

Lead security investigations for AI systems used in scientific discovery. Develop threat models, test defenses through adversarial assessments and technical analysis, define security requirements, evaluate evidence, and drive improvements with Security, Safety, and engineering teams. The role addresses dangerous misuse, model and data compromise, deployment risks, and safeguards for increasingly capable AI systems.

Build and maintain software, services, data pipelines, and model integrations for AI safety evaluations, red teaming, mitigation experiments, and scientific AI research. Translate researcher needs into reliable tools, preserve reproducible experiment records, and own systems from prototyping through deployment, testing, monitoring, documentation, and improvement. Collaborate with AI and software engineering teams on shared infrastructure.

Develop and implement safety mitigations for frontier scientific AI models, agents, tools, and laboratory-connected workflows. Translate risk assessments and policy requirements into safeguards spanning training, classifiers, monitoring, access controls, and runtime interventions. Design experiments, evaluate robustness and legitimate-use impacts, investigate failures and distribution shifts, and integrate mitigations with model training, inference, platform, and evaluation teams.

Own Coupa procure-to-pay configuration through implementation and go-live. Coordinate workshops, configure approvals, catalogs, punch-outs, and supplier onboarding; manage data migration, UAT, reconciliation, defect resolution, and systems integrator activities. After launch, triage issues, implement configuration or training fixes, and deliver documentation and handover to internal owners.

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

Build provenance infrastructure for scientific data and workflows, including schemas, metadata systems, Python libraries, APIs, and durable records of agent activity. Support reproducible ML, simulation, and drug discovery workflows while transforming research prototypes into scalable production services. Establish engineering practices for testing, packaging, CI, releases, and documentation across local, cloud, and GPU environments. Collaborate closely with scientists and platform teams on storage, compute, and orchestration systems.

Own end-to-end mechanical design of custom mobile manipulators by integrating robot arms with AMR/AGV bases. Create CAD models, mounting structures, cable routing, simulation-ready geometry, and mass, payload, and center-of-gravity analyses. Iterate multiple design concepts, document tolerances and assembly procedures, and support physical builds. Collaborate with simulation and platform evaluation engineers during validation. This is a hands-on mechanical and mechatronic design role focused on hardware integration rather than robotics software or controls.

Build and adapt AI models for customer-specific scientific workflows. Lead post-training using SFT and reinforcement learning, design evaluation systems, analyze model failures, and drive iterative quality improvements. Partner with AI researchers and software engineers to integrate reliable model capabilities into production workflows. The role requires strong Python and PyTorch skills, deep familiarity with language, multimodal, or agentic models, and the ability to translate customer needs into technical solutions.

Lead applied AI model post-training, evaluation, and production ML system development. Adapt models to customer-specific scientific workflows using SFT and reinforcement learning, build evaluation and monitoring systems, debug complex model behavior, collaborate with research and software teams, and mentor engineers. The role requires deep expertise in Python, PyTorch, LLMs, multimodal or agentic systems, and shipping reliable machine learning capabilities.

Owns go-to-market strategy, positioning, launches, offer development, case studies, and sales enablement for Lila Sciences’ life sciences product portfolio. The role translates AI, autonomous lab, and scientific capabilities into compelling narratives for biopharma and R&D buyers. It partners with Product, Commercial, Business Development, and Brand, synthesizes customer and competitive insights, and supports sales motions with major pharmaceutical accounts.