Staff AI/ML Platform Engineer

Albert Invent is on a mission to digitalize the world of chemistry through data and machine learning, enabling R&D organizations to accelerate the invention of new materials. As an AI/ML Platform Engineer, you will build foundational systems that enable AI capabilities for scientists, including APIs, data pipelines, and workflow orchestration.

Responsibilities:

• 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
• 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
• 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
• 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
• 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

Requirements:

• 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
• 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
• 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