Top Tech Jobs & Startup Jobs

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.

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.

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 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.