7+ years of software engineering experience with a Bachelor's or 5+ years with a Master's or PhD.
Deep expertise in Python backend development, including async programming and performance optimization.
Experience with REST API construction using frameworks like FastAPI.
Proficient in data engineering practices, including building and maintaining data pipelines and working with diverse data stores.
Familiarity with workflow orchestration tools like Ray and Temporal, and experience operating production systems at scale.
Responsibilities
Develop high-performance Python APIs for AI model serving and workflow management.
Architect scalable and reliable backend services for low-latency operations.
Build and maintain long-running data pipelines and workflow orchestration.
Ensure system reliability and implement observability features for troubleshooting and monitoring.
Collaborate with ML researchers and product engineers to deliver production-ready infrastructure.
Benefits
Flexible remote work culture with a global team.
Inclusive and diverse environment where employees can be their authentic selves.
Commitment to employee well-being and work-life balance.
Opportunities to influence important architectural decisions in AI infrastructure.
Full Job Description
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)
