Jobs at Lila Sciences

Provides legal counsel to AI research, product, data, safety, and security teams developing frontier scientific AI systems. Advises on training data, copyright, licensing, privacy, model releases, open-source strategy, research collaborations, autonomous experimentation, biosecurity, AI regulation, export controls, infrastructure, and regulatory inquiries. Builds scalable legal frameworks, playbooks, templates, and self-service guidance for a rapidly evolving AI and life sciences organization.

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.

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.

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.

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.

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.

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.

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.

The Senior Software Engineer will develop systems for scientific workflows, APIs, and applications, collaborating across teams to enhance performance and reliability in production environments.

The Staff Engineer will lead technical direction on data infrastructure, focusing on ingestion frameworks, storage architecture, and orchestration to support scientific workflows. Responsibilities include mentoring, ensuring reliability, and collaborating with cross-functional teams to establish coding standards and data integrity.