About the role
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!
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.