Top Tech Jobs & Startup Jobs

Operate and recover automated scientific workcells during a permanent shift, troubleshoot instrument, software, and run failures, and drive reliability and throughput improvements. Coordinate downtime, maintenance, vendor service, and cross-disciplinary root-cause investigations. Execute calibrations, improve diagnostic tools and recovery playbooks, document events, and conduct structured shift handoffs. The role supports live production in an on-site Cambridge laboratory environment.

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.

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.