Jobs at SandboxAQ

Lead product marketing and demand generation for AI Simulation offerings: craft positioning and messaging, produce campaign and sales enablement assets, plan product launches, run outbound and nurture programs, and translate complex technical science into buyer-facing value to drive awareness and pipeline.

Lead product for the Data Platform supporting AISim's BioSim and ChemSim: define roadmap for ingestion, auto-detection, schema reconciliation, lineage, provenance, and governance. Build partner ingestion templates, developer APIs, federated data-sharing, and tracking metrics (time-to-usable, auto-match accuracy, lineage completeness). Partner with engineering, science, design, and compliance to turn ambiguous data problems into scalable, measurable products.

Lead product strategy and roadmap for a shared core platform underpinning BioSim and ChemSim. Hire and grow a 5-6 person PM team, drive build-vs-buy decisions, prioritize cross-domain customer needs, establish data-driven operating rhythms and platform OKRs, and stay hands-on during team ramp.

Lead the Data Platform product for AISim, owning ingestion, schema reconciliation, lineage/provenance, sensitivity and field-of-use governance, federated data sharing, experiment-integration APIs, and dataset usability metrics. Partner with engineering, science, design, and compliance to turn ambiguous data problems into measurable, scalable platform features.

Lead FP&A activities including annual and rolling forecasts, budgeting, variance analysis, program-level financial reporting, and building scalable models and dashboards. Partner with business leaders, operations, GTM, accounting and legal to translate strategy and customer commitments into financial plans, revenue commitments, and program performance metrics while strengthening FP&A controls and automation in a high-growth environment.

Design and implement computational workflows to investigate homogeneous catalytic cycles, support catalyst discovery and optimization with industry partners, develop large-scale quantum chemistry datasets and ML models, collaborate with platform and engineering teams to improve discovery workflows, lead client engagements, and mentor junior scientists while communicating results via reports and publications.

Lead architecture and scaling of cloud-native infrastructure to support large AI workloads. Mentor engineers, decompose complex systems, design foundational platform services, and collaborate with product, research, and security teams to ensure secure, performant, and highly available GPU/ML infrastructure.

Technical leader for Platform & Infrastructure building scalable, resilient cloud-native systems to support large AI workloads. Define architecture, mentor engineers, solve systemic infrastructure bottlenecks, design foundational platform services, and collaborate cross-functionally to ensure secure, high-performance operations.

Lead HR Operations for North America, owning HRIS, workflows, policy translation, employee lifecycle processes, contractor populations, and sensitive employee relations. Improve systems, introduce AI/automation, manage and develop junior HR Ops staff, partner cross-functionally, and drive strategic projects to scale operations for rapid growth and M&A integration.

The Manager of Clinical Research leads AQMed's clinical program, overseeing multi-center studies globally, managing external partners, and ensuring regulatory compliance while integrating AI tools to enhance efficiency.

The Director of Product Management for AI Generation Engine will lead product strategy and execution in Finance & Risk Modeling, focusing on innovative AI solutions and close collaboration with financial services clients.

The Data Engineer will develop and maintain data pipelines, build data models, ensure data quality, and support cross-functional teams while improving internal data platform infrastructure and documentation.

Lead the Magnets vertical: define scientific roadmap, build and mentor a multidisciplinary team, integrate first-principles, multi-scale and ML modeling with experimental DBTL, manage IP translation and external partnerships, and drive commercialization and program engagements with national labs, industry, and defense partners.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

Translate cutting-edge ML and physics research into production-grade scientific code, architect and scale large simulation and training pipelines on GPU infrastructure, optimize hardware-level performance, support models through full product lifecycle, and integrate complex datasets and ML models into external-facing drug and materials discovery products.

Design, scale, and productionize ML and physics-based scientific code for drug and materials discovery. Transition prototypes to robust products, orchestrate distributed GPU training, implement hardware optimizations, and support model lifecycle and integration into large-scale simulation frameworks.

Translate cutting-edge ML and physics research into production-grade scientific code, architect and scale large-scale simulation and training pipelines on GPU infrastructure, optimize hardware-level performance, and support models throughout the product lifecycle to accelerate in silico drug and materials discovery.

The Director of Product Management will lead product strategy for AI solutions in finance, focusing on innovation and collaboration across disciplines while managing customer relationships and rapid prototyping of financial applications.

The Machine Learning Engineer will manage the ML lifecycle, develop models, ensure performance, and collaborate with a diverse team to build AI-first products.

The role involves end-to-end machine learning lifecycle management, including data processing, model development, rigorous testing, and production deployment, focusing on large quantitative models for AI-first products.