Staff ML Ops Engineer

Albert’s mission is to digitalize the world of chemistry. Using data and machine learning, Albert enables R&D organizations to dramatically accelerate the invention of new materials. Our platform helps scientists and engineers build structured data foundations, digitize formulation and testing workflows, and apply AI to innovate faster, smarter, and at scale. About the role As our Backend & Infrastructure Engineer, you will architect and build the core systems that power everything our AI/ML team delivers—the APIs, infrastructure, and distributed systems that make intelligent capabilities possible at scale. This is a foundational role: you'll shape how AI gets built and shipped here.  We are seeking a highly motivated and talented individual with deep expertise in Python backend development, Kubernetes, and distributed systems. You'll be embedded with ML engineers and researchers, building robust systems that turn ambitious AI ideas into production realities—whether that's powering agent-based workflows, scaling inference, or enabling scientific computing pipelines. The infrastructure you build will directly enable researchers at the world's largest chemical and materials companies to leverage AI in ways that weren't possible before—accelerating discovery, enabling inverse design of novel materials, and transforming how science gets done. What you'll do Infrastructure & Kubernetes:  Design, deploy, and maintain Kubernetes infrastructure supporting AI/ML workloads  Manage containerized services, autoscaling, networking, and resource optimization  Backend Development:  Design and build high-performance Python APIs and services using FastAPI or similar frameworks  Architect backend systems for scalability, reliability, and low latency  Build integrations between AI/ML systems and the broader Albert platform  Distributed Systems:  Build and operate distributed systems that handle compute-intensive and high-throughput workloads  Design for fault tolerance, graceful degradation, and horizontal scalability  Implement async workflows, job queues, and task orchestration as needed  Data Infrastructure:  Architect and maintain data pipelines and storage systems supporting AI/ML workflows  Work with vector databases, caches, and other data stores as required by ML systems  Ensure efficient data access patterns for training and inference workloads  Reliability & Operations:  Implement observability including logging, metrics, tracing, and alerting  Own system reliability—troubleshoot issues, conduct post-mortems, and continuously improve  Design CI/CD pipelines and promote automation best practices  Implement infrastructure-as-code practices using Terraform, Helm, ArgoCd, Pulumi, or similar tools  Collaboration:  Partner closely with ML engineers to understand requirements and deliver production-ready infrastructure  Translate ML prototypes and research code into scalable, maintainable systems  Contribute to technical decisions that shape the team's architecture  You will have Deep expertise in Python backend development and distributed systems  Strong Kubernetes and cloud infrastructure experience  A builder's mindset—you want to create foundational systems that others build on  Genuine interest in science and technology; curiosity about how your work enables scientific discovery  A commitment to building systems that are reliable, maintainable, and scalable  Key competencies A degree in Computer Science or a related field with 7+ years of industry experience (Bachelor's) or 5+ years (Master's or PhD) in software engineering  Experience supporting AI/ML teams or deploying ML systems in production  Experience with GPU workloads and scheduling  Advanced proficiency in Python including async programming and performance optimization  Deep experience with Kubernetes—cluster management, networking, autoscaling, and troubleshooting  Strong background in distributed systems and microservices architecture  Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code  Proficiency in REST API development using FastAPI, Flask, or similar  Experience with containerization and CI/CD pipelines  Track record of operating production systems at scale   Preferred/Bonus Points Familiarity with scientific computing or research environments  Background in or curiosity about chemistry, materials science, or related fields  Familiarity with data engineering tools (Airflow, Dagster, or similar)  Experience with vector databases or search infrastructure  Expertise in observability tools (Prometheus, Grafana, Datadog)  Experience with message queues and event-driven architectures (Kafka, Redis, RabbitMQ)  Contributions to open-source projects  Experience mentoring engineers  Why Albert? We have a huge impact. Albert is a growing team with a big reach. Our Platform facilitates the invention of materials for tens of thousands of companies and hundreds of thousands of applications - from coatings used on rockets to adhesives used in electric vehicles to 3D printed medical devices. We love distributed teams. Albert’s home-base is in the California Bay Area, but we have multiple offices and employees sprinkled around the globe. In fact, over 50% of our employees work outside of California! An international remote culture is in our DNA. We care about you. Albert works hard to create a positive environment for our employees, and we think your life outside of work is important too. We work hard and we play hard. We value diversity. Growing and maintaining our inclusive and diverse team matters to us. We are committed to being a company where our employees feel comfortable bringing their authentic selves to work and have the ability to be successful -- every day. We’re always looking for humble, sharp, and creative folks to join the Albert team. If you think you might be a fit please apply! Locations: Remote (United States); Oakland, CA