Jobs at SandboxAQ

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.

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.

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.

As a Staff Forward Deployed Engineer, you'll bridge technical gaps between large models and clients, ensuring successful deployments in drug discovery and simulations.

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 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 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.

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.