Jobs at SandboxAQ

Lead a small AI engineering team while remaining hands-on in research and production software development. Build agentic capabilities for monitoring, guardrailing, evaluation, collaboration, and security. Perform exploratory data analysis, feature engineering, model fine-tuning, prototyping, and technical design. Translate AI research into scalable product features, set technical direction, mentor engineers, and deliver reliable production code.

Develop and optimize generative AI architectures for material and molecular discovery, including diffusion models, flow matching, and GFlowNets. Integrate reward signals from learned predictors and simulations, manage data ingestion and quality control, develop computational workflows, and deploy production-grade ML capabilities. Collaborate with ML researchers, computational scientists, engineering, product, and R&D teams. The role requires expertise in applied ML, scalable frameworks, cloud platforms, and material discovery, with preferred experience in active learning, Bayesian optimization, guided generation, and agentic or LLM methods.

Build production agentic AI capabilities focused on monitoring, guardrails, evaluation, security, collaboration, and ROI. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and deliver production-ready code while exploring emerging agentic technologies and AI security techniques.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Develop and deploy machine-learned interatomic potentials for PFAS substitution. Responsibilities include generating and curating first-principles training data, building reproducible cloud and HPC workflows, training and validating ML force fields, running molecular dynamics and quantum chemistry campaigns, predicting physical properties, extending models to condensed-phase and process chemistries, and communicating model uncertainty and domain limitations.

Lead a small team developing secure, reliable agentic AI capabilities. Responsibilities include setting technical direction, building agent monitoring, evaluation, and guardrail frameworks, performing machine learning tasks, translating research into production features, mentoring engineers, and contributing hands-on code and technical designs. The role focuses on agent collaboration, trust, security, quality measurement, and scalable software delivery.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform exploratory data analysis, feature engineering, and model fine-tuning; translate research into scalable software; prototype rapidly; and ship reliable code. The role requires strong AI/ML and software engineering expertise, Python skills, cross-functional collaboration, and interest in AI security, agent orchestration, red teaming, and secure model deployment.

Lead a small team developing secure, reliable agentic AI capabilities. Responsibilities include building agent monitoring, guardrails, evaluation frameworks, and collaboration tools; performing machine learning tasks such as exploratory data analysis, feature engineering, and fine-tuning; translating research into production features; contributing code and technical designs; and mentoring engineers while setting technical direction.

Designs and manages multi-cloud infrastructure across GCP, AWS, and Azure using Terraform and Kubernetes. Provides hands-on developer support, troubleshoots infrastructure and cluster issues, and maintains highly available systems. Responsibilities include networking, container orchestration, monitoring, logging, CI/CD automation, documentation, security collaboration, and on-call participation.

Own and negotiate complex commercial transactions, including customer agreements, research collaborations, partnerships, data licenses, SaaS contracts, vendor agreements, and professional services agreements. Partner with sales, science, finance, and R&D teams, particularly on life sciences and computational chemistry deals. Build scalable legal infrastructure through playbooks, templates, fallback positions, self-service processes, and AI-enabled contract workflows.

Own the catalysis product strategy, roadmap, and commercial expansion for an AI-driven materials discovery platform. Translate CHIPS Act objectives into product requirements and fulfillment evidence, define scientific datasets and model benchmarks, guide reaction-level modeling, and quantify customer value. Partner with scientists, program managers, business development teams, industrial customers, semiconductor organizations, catalyst manufacturers, and national laboratories. Lead segmentation, packaging, pricing, strategic partnerships, KPIs, and build-versus-buy decisions across refining, chemicals, emissions control, semiconductors, and energy-transition markets.

Lead SandboxAQ’s brand and visual design practice from the ground up. Set creative direction, develop brand systems and sub-brand architecture, design and maintain the Webflow website, and create high-quality marketing and corporate materials. Build scalable templates, asset libraries, and AI-assisted workflows while partnering with product, marketing, and go-to-market teams. Translate complex scientific and technical concepts into clear visual designs and direct external creative partners when needed.

Senior Product Designer shaping AI agent platforms for developers and enterprise users. Responsibilities include defining product direction, designing and prototyping coded experiences across command-line, web, console, collaboration, onboarding, and documentation surfaces, developing design systems, conducting user research and usability testing, analyzing product analytics, creating data visualizations, and collaborating with Product, Engineering, design, and go-to-market teams.

Own and build the AI Simulation marketing operations and analytics function, including martech architecture, campaign instrumentation, attribution, funnel reporting, lifecycle programs, lead scoring, data governance, and Salesforce integrations. Partner with Marketing, RevOps, Sales, Finance, and Product to establish trusted measurement, connect marketing activity to pipeline and revenue, and improve investment decisions through reporting and actionable insights.

Leads SandboxAQ’s global marketing events and thought-leadership program, overseeing approximately 40 annual virtual and in-person events. Responsibilities include developing event strategy, managing vendors and logistics, owning budgets and infrastructure, generating and measuring leads and pipeline through Salesforce and Marketo, reporting performance, and coordinating with Sales, executives, marketing teams, agencies, and external partners. The role also manages event materials, shipping, swag, inventory, and scalable processes.

Own and develop SandboxAQ’s intellectual property function, including patent portfolio strategy, invention harvesting, prosecution, lifecycle management, outside counsel oversight, freedom-to-operate analyses, trademark clearance, and trade secret programs. Partner with scientists, engineers, and business teams to protect innovations, inform product strategy, support transactions, and build scalable IP processes in a fast-growing AI-focused company.

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 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.

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.

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