[Hiring] Staff AI/ML Platform Engineer @Albert Invent

Role Description

As our AI/ML Platform Engineer, you will build the foundational systems that enable AI at the bench—the APIs, data pipelines, and workflow orchestration that turn ambitious AI capabilities into tools scientists can actually use.

We're building the lab of the future: a system where AI helps scientists invent faster, discover more, and optimize better. That future depends on a robust platform that can serve models in real-time, orchestrate complex multi-step pipelines, and integrate seamlessly with the data infrastructure that gives AI its understanding of chemistry. You'll be at the center of that work.

This is a foundational role with real influence. Our platform is robust but still in its early days—you'll have the opportunity to shape architecture decisions, introduce new technologies, and build systems that directly enable researchers at the world's largest chemical companies to leverage AI in ways that weren't possible before.

At Albert Invent, we value curiosity, rigor, and openness. We're writing the playbook for AI-augmented science together. If you want to build the infrastructure that makes that possible, we'd love to hear from you.

What you'll do

• Own the APIs, data pipelines, and workflow orchestration that power our AI products—from real-time model inference to long-running optimization pipelines.
• Build the services that serve up models, manage workflows, and connect AI capabilities to the structured data that makes them useful.
• Work closely with our Active Learning and LLM/Agents team leads, translating their product vision into scalable, production-grade systems.
• Power model playgrounds for chemists, inverse design pipelines, and orchestrated agent workflows.

API & Backend Development

• Design and build high-performance Python APIs that serve models, manage workflows, and expose AI capabilities to the broader platform.
• Architect backend services for scalability, reliability, and low latency.
• Build integrations between AI/ML systems, graph databases, and external data sources.

Pipeline & Workflow Orchestration

• Build and maintain long-running workflow pipelines using Ray and Temporal.
• Design orchestration patterns for multi-step agent pipelines, batch inference, and numerical optimization workflows.
• Ensure fault tolerance, graceful degradation, and efficient resource utilization.

Data Infrastructure

• Architect and maintain data pipelines that feed AI/ML workflows.
• Work with Neptune (graph), Redis, DynamoDB, and other data stores to enable efficient data access patterns.
• Build the connectors and transformations that give AI systems access to clean, structured, trusted data.

Platform 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.

Collaboration & Influence

• Partner with ML researchers, data scientists, and product engineers to understand requirements and deliver production-ready infrastructure.
• Collaborate closely with Active Learning and LLM/Agents team leads to align platform capabilities with product needs.
• Contribute to architectural decisions that shape how AI gets built and shipped at Albert.

Qualifications

• Deep expertise in Python backend development and building production APIs.
• Experience designing and operating data pipelines and workflow orchestration systems.
• A builder's mindset—you want to create foundational systems that others build on.
• Genuine curiosity about how your work enables scientific discovery.
• A commitment to rigor: AI makes mistakes confidently, and our customers won't accept hand-waving—neither should we.

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.
• Advanced proficiency in Python including async programming and performance optimization.
• Experience building and maintaining REST APIs using FastAPI or similar frameworks.
• Experience with workflow orchestration tools (Ray, Temporal, or similar).
• Strong background in data engineering: pipelines, transformations, and working with diverse data stores.
• Experience with cloud platforms (AWS preferred) and containerization (Docker, Kubernetes).
• Familiarity with graph databases, key-value stores, or other NoSQL systems (Neptune, Redis, DynamoDB a plus).
• Track record of operating production systems at scale.

Preferred/Bonus Points

• Experience supporting AI/ML teams or deploying ML systems in production.
• Familiarity with distributed computing frameworks (Ray, Dask, Spark).
• Experience with GPU workloads and scheduling.
• Background in or curiosity about chemistry, materials science, or scientific computing.
• Experience with observability tools (Prometheus, Grafana, Datadog).
• Experience with message queues and event-driven architectures.
• Contributions to open-source projects.
• Experience mentoring engineers.

Benefits

• 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.
• 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.
• An international remote culture is in our DNA.
• We care about you. Albert works hard to create a positive environment for our employees.
• We value diversity. Growing and maintaining our inclusive and diverse team matters to us.