Jobs at SandboxAQ

Act as the technical lead between LQMs and client contracts: diagnose workflows, design and deploy software and infrastructure, wrap scientific logic, integrate agentic systems, and own end-to-end compute and data pipeline deployments to enable scientific delivery.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

The Senior Backend Engineer will develop core functionalities for AI-driven drug discovery, collaborating with R&D and product teams, managing infrastructure, and ensuring engineering excellence.

The Senior Forward Deployed Engineer will deploy and validate AQNav systems, drive success during tests, communicate with stakeholders, and bridge operations with research and engineering.

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 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 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-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.

Lead execution of high-uncertainty R&D programs by building adaptable roadmaps, anticipating bottlenecks, translating between scientists/product/finance, driving resourcing decisions, and orchestrating last-mile launches for milestones, papers, or product moments in AI-driven drug discovery.