SandboxAQ

Research Scientist

• Implement, experiment with, and refine deep learning models for protein-ligand co-folding and structure prediction
• Design and execute systematic evaluation pipelines to measure model performance against state-of-the-art methods and internal benchmarks
• Collaborate with senior scientists and engineers to integrate validated models into production-ready drug discovery workflows
• Employ computational and data analysis techniques to generate insights from structural and sequence datasets
• Present research progress through internal scientific talks, technical write-ups, and contributions to peer-reviewed publications
• Work closely with multidisciplinary teams to prototype and scale impactful solutions

ML Research Scientist, Co-Folding and Affinity

Technical Program Manager

• Own the catalysis validation program plan end-to-end: campaign sequencing, partner SOWs, sample logistics, data ingestion timelines, and milestone tracking
• Manage external validation partners including contracting cadence and deliverable acceptance
• Coordinate internal hand-offs across Research Scientists and Computational/ML Scientists
• Operationalize the design-test-make-deploy pipeline for this vertical
• Drive risk, schedule, and resource trade-offs with the Vertical Lead

Technical Program Manager, Catalysis

• Lead the translation of reaction energetics derived from atomistic simulations and machine learning potentials into process-scale predictions.
• Own the design and execution of microkinetic and reactor-level modeling workflows for industrially relevant catalytic chemistries.
• Connect atomistic simulation outputs to activity, selectivity, and catalyst lifetime predictions.
• Partner with industrial validation partners to test and refine models against real-world data.
• Mentor junior scientists and shape the technical roadmap for microkinetic capability within SandboxAQ.

Staff Research Scientist, Catalyst Simulation

Clinical Research

• Own end-to-end execution of AQMed's clinical study portfolio, from feasibility through pivotal and across the US, Europe, and Asia.
• Develop and maintain clinical study plans, protocols, budgets, agreements, timelines; proactively identify and resolve threats to enrollment, data quality, and regulatory timelines.
• Lead site identification, qualification, initiation, monitoring, and close-out across multi-center, multi-geography studies.
• Serve as the primary operational owner of all IRB/ Ethics Committee submissions, amendments, and ongoing reporting requirements.
• Select, onboard, and manage external clinical consultants and/or CRO partners; hold them to high standards of quality, speed, and accountability.
• Define clear scopes of work, success metrics, and escalation paths for all external clinical resources.
• Build and sustain trusted relationships with clinical investigators, coordinators, and hospital administration at each study site.
• Drive enrollment with creativity and urgency; designing site-specific strategies, troubleshooting bottlenecks, and escalating risks early.
• Collaborate with Product, Data Science, and Engineering to ensure study designs generate data that is both clinically meaningful, actionable and useful.

Manager of Clinical Research

Director

• Develop and help execute SandboxAQ’s corporate development strategy, including identifying, prioritizing, and evaluating acquisition, investment, and strategic partnership opportunities aligned to company goals.
• Lead end-to-end transaction and partnership processes, from opportunity sourcing and market mapping through diligence, business case development, negotiation, internal alignment, and execution.
• Build the strategic and financial narratives that support executive decision-making, including market analysis, competitive intelligence, business model evaluation, partnership theses, and financial modeling.
• Partner with the COO and senior leadership to assess where SandboxAQ should buy, build, partner, or invest in order to accelerate growth and sharpen market position.
• Own and manage relationships with selected strategic partners, including major technology, ecosystem, channel, and commercial partners, and help structure partnerships that create measurable business impact.
• Work cross-functionally with product, research and development, engineering, and business teams to translate technical capabilities into compelling external partnership and growth opportunities.

Director of Corporate Development – Strategic Partner Management

• Develop large-scale datasets and machine-learned force fields or potentials for materials discovery.
• Deploy and support the design and implementation of computational workflows simulating the reactivity and dynamics of catalytic materials across atomic, mesoscale, and continuum scales.
• Collaborate with platform, machine learning, and software engineering teams to continuously improve the efficiency and performance of discovery workflows.
• Support client engagements focused on the research, development, discovery, and optimization of novel heterogeneous catalyst materials.
• Prepare and deliver high-impact reports, presentations, and publications to communicate research findings to internal, academic, and industry partners.
• Mentor junior scientists and interns in catalysis, reaction engineering, and computational methods, championing best research and software practices.

Research Scientist, Catalyst Simulation

Full-stack Engineer

• Maintain and extend existing scientific software packages, and drive the full lifecycle of new ones:
• Architect and implement cutting-edge scientific and ML software alongside subject matter experts
• Adopt current practices and ensure stability through testing
• Work closely with the Engineering Team to deploy these modules into the SandboxAQ’s simulation platform:
• Manage dependencies, containerize, and integrate software into CI/CD pipelines
• Integrate with the compute and data backends
• Define API endpoints and schemas
• Collaborate with the engineering team to build UI components
• Maintain and support production deployments of scientific software packages
• Foster a work culture of curiosity and kindness

Senior Scientific Software Engineer, AI Simulation

Machine Learning Engineer

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks
• Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives
• Lead the effort in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy
• Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context
• Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps
• Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment
• Drive technical execution with high autonomy, making critical design and implementation decisions independently

Staff Machine Learning Engineer, AI Generation Engine

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks.
• Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives.
• Contribute to the efforts in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy.
• Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context.
• Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps.
• Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment.

Senior Machine Learning Engineer – AI Generation Engine

Product Manager

• Own the product strategy and execution for Finance & Risk Modeling
• Lead rapid prototyping and innovation across a broad range of new ideas
• Operate in a highly ambiguous, fast-changing environment
• Identify, evaluate, and prioritize new AI / LQM use cases
• Serve as a trusted product partner to senior stakeholders

Director of Product Management, AI Generation Engine

• Own the Platform Architecture: Serve as the final technical authority for the end-to-end software stack, spanning embedded firmware, in-field Linux devices, cloud data pipelines (AWS/GCP), and the clinician-facing web application.
• Product-Ready Foundation: Design clear and flexible interfaces such that our platform can evolve with our product roadmap without major refactors.
• Data Path Integrity: Design and maintain a HIPAA-compliant, production-grade data platform that ensures data confidentiality, integrity, and availability from the sensor to the cloud.
• Hands-on Engineering: This is a player-coach role. You will write code, review complex PRs, debug hardware-software integration issues in the lab, and configure CI/CD pipelines. You lead by doing, setting the standard for code quality and testing. You know what “good” looks like.
• Technical Mentorship: Mentor a growing team of senior and staff engineers, fostering a culture of technical excellence, rigorous review, and psychological safety.
• Drive Quality & Compliance: Lead the engineering team’s adherence to applicable medical device standards (e.g. IEC 62304, ISO 14971) and regulations. You are the gatekeeper ensuring that design outputs (code) meet design inputs (requirements) and that our "move fast" culture never compromises patient safety and critical product functions. Scale strong engineering practices across the team, including design reviews, code review quality, test strategy and technical documentation.
• Cross-functional execution: Partner with Product, Clinical and Regulatory stakeholders to translate product requirements, clinical performance goals and regulatory inputs into concrete, actionable technical plans, engineering specifications and development timelines. Ensure that our software architecture, implementation and documentation support our clinical study needs and regulatory plans (e.g. 510(k) and De Novo).

Principal Software Engineer, Medical Devices

Senior Machine Learning Engineer, AI Generation Engine

• Partner with ML and Computational Chemistry researchers to translate internal scientific code and prototypes into robust, production-ready software systems for external product offerings.
• Implement and integrate core scientific algorithms (ML, deep learning, physics-based simulation) into scalable product platforms.
• Collaborate within a multi-disciplinary "pod" of engineers, computational chemists, AI experts, and product managers to define requirements, architecture, and deployment strategies for new features.
• Ensure the scientific integrity and accuracy of research models (e.g., active learning, FEP, etc.) when deployed in production.
• Drive the product lifecycle from scientific inception to external deployment, ensuring technical excellence and market fit.

Product Research Scientist, Drug Discovery

Data Engineer

• Data Pipeline Development & Maintenance: Work across a mixed-maturity pipeline environment
• Data Modeling: Build and optimize data models that serve a diverse set of consumers. You'll make the data accessible and trustworthy, not just available
• Simulation Data Integration: Work within the in-house simulation suite to add data-capturing capabilities and ensure simulation outputs feed cleanly into downstream pipelines alongside real-world field data
• Data Quality & Observability: Instrument pipelines with quality checks, anomaly detection, and alerting so issues surface early
• Cross-Functional Data Support: Translate ambiguous asks into well-defined requirements, repeatable datasets and lightweight Dashboards that the team can use independently going forward
• Data Platform Infrastructure Contribution: Improve the features and reliability of our internal data platform over time
• Documentation: Own the technical documentation for pipelines, data models, and schemas you touch. In a team this cross-functional, good documentation is a force multiplier

Artificial Intelligence

• Provide operational support and leadership to BioSim and ChemSim divisions, running day-to-day business operations including identifying synergies in resource allocation and operationalizing scaling systems.
• Lead the transition from high-growth experimentation to scaled operations by optimizing talent density and capacity across PhDs, SWEs, and GTM specialists.
• Be responsible for hands-on and end-to-end execution, track velocity across complex milestones, and rapidly shift resources (financial and talent) as requirements arise and shift.
• Spearhead the Chemical Simulation business unit during its expansion.