About SandboxAQ
SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.
We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.
At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.
About the Role
SandboxAQ is seeking a Staff MLOps engineer to mature our MLOps practices by building out infrastructure and application code, as well as embedding with R&D teams to champion adoption of your tools and gather feedback to improve them. In this role, you’ll be working closely with data engineers, software developers, research scientists, and ML scientists to deliver solutions at the frontier of AI in chemistry and the life sciences. This is a hands-on-keyboard role, however it includes the opportunity to build and lead a team of MLOps engineers for interested candidates.
You’ll bring broad experience with infrastructure for automated training, evaluation, and maintenance of trained models; systems for efficient inference on trained models; and practices for versioning dataset and models. Ideal candidates will bring a blend of experience with highly-automated, stable MLOps pipelines for highly-available systems, and scrappy ad-hoc pipelines for R&D projects. Most importantly, you’ll bring a track record of working in a fast-moving software development team, exploring new technologies, and solving problems across an entire software stack.
What You’ll Do
- Mature our MLOps practice by defining processes and building fundamental tooling.
- Embed closely with R&D teams to assist in delivering project goals and drive adoption of practices and tooling.
- Drive the design and implementation of complex, security-sensitive data processing and storage systems with complex tenancy and data isolation requirements.
- Collaborate closely with the product team and internal stakeholders in all phases of software development to validate the solutions you propose and implement.
- In collaboration with the rest of the engineering team, build and manage infrastructure for SandboxAQ’s simulation and data platform.
- Review code and participate in design and architectural discussions.
About You
- 7+ years of experience with MLOps fundamentals:
- Automated training, evaluation, and retraining loops
- Dataset and model versioning tools, Weights & Biases preferred
- Systems for serving inference
- Build end-to-end ML pipeline using industry-standard tools.
- Deep experience with at least one major cloud provider. GCP preferred.
- Experience with managing complex data governance requirements.
- Familiarity with MLOps architectures and best practices for LLMs and Agentic systems.
- 3+ years of experience with infrastructure as code management of public cloud providers. Familiar with terraform. GCP preferred.
- Familiarity with building and maintaining CI/CD pipelines for ML systems.
- 3+ years of experience with Python, with strong knowledge of software design principles.
- Familiarity with building data pipelines or data processing systems at scale, including orchestration tools like Airflow.
- Excellent communication and collaboration skills, with the ability to effectively influence a cross-functional team.
Nice to have
- Domain experience in advanced materials, drug discovery, cheminformatics, or other areas of chemistry or biology, especially experience with AI systems applied to these domains.
- Experience with AI applications in knowledge graphs.
- Experience profiling and optimizing GPU usage in MLOps applications.
The US base salary range for this full-time position is expected to be $225k - $295k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.
