Senior Staff AI Engineer

Job description

About the role We are seeking a highly motivated and talented individual with a passion for AI/ML engineering and agent technologies. In this role, you will unleash your creativity, intelligence, and curiosity to build scalable AI systems that empower researchers and chemists at leading chemical and materials organizations to push the boundaries of innovation. As an ML Engineer specializing in LLMs and agent technologies, you will play a critical role in our mission to streamline workflows and provide robust, scalable solutions that support AI/ML capabilities for thousands of researchers worldwide. This role is central to the development of autonomous systems and tools tailored to chemical and materials science applications. What you'll do We are seeking an exceptional ML Engineer with a focus on LLMs and RAG systems. This role prioritizes designing and developing scalable, fault-tolerant AI systems while maintaining a strong focus on domain-specific AI solutions. You will play a critical role in building robust infrastructure to support high-performance applications and tools, enabling seamless data integration and transformation to power AI/ML capabilities in chemical and materials science. Scalable AI System Development: Design, build, and maintain scalable, fault-tolerant AI systems leveraging OpenAI and Anthropic models. Develop RAG architectures to ensure efficient, high-performance information retrieval tailored to chemical and materials science. Optimize system performance to handle large-scale data and application demands. AI Agent Development: Build and maintain intelligent AI agents using modern frameworks. Collaborate with domain experts to refine agent capabilities for specific scientific workflows. Data Engineering and Integration: Architect and maintain vector database solutions for efficient data storage and retrieval. Develop pipelines for ingestion, transformation, and storage to enable AI/ML workflows. Collaborate with platform and ML engineers to integrate AI/ML models with backend systems. System Reliability and Fault Tolerance: Implement robust error-handling, monitoring, and alerting mechanisms to ensure system resilience. Troubleshoot and resolve system bottlenecks and failures. CI/CD and Deployment Pipelines: Design, implement, and maintain CI/CD pipelines for AI systems and data workflows. Promote automation and best practices to enhance the development lifecycle. Adoption of Emerging Technologies: Stay informed on the latest trends and tools in AI/ML engineering and agent technologies. Introduce and implement new technologies to improve system scalability, data integration, and developer productivity. Collaboration and Cross-Functional Teamwork: Work closely with AI/ML, data engineering, and platform teams to understand and deliver on technical requirements. Contribute to architectural decisions that impact the overall platform ecosystem. You will have A strong passion for AI/ML engineering and scalable data systems. An ability to prioritize system scalability and fault tolerance while focusing on innovative AI/ML solutions. A collaborative mindset and excellent communication skills. A commitment to quality and innovation in AI and data engineering. Key competencies A degree in Computer Science, AI, or a related field with 7+ years of industry experience (Bachelor’s) or 5+ years (Master’s or PhD) in software engineering, emphasizing expertise in building scalable, fault-tolerant AI systems. Advanced knowledge of modern AI frameworks (e.g., LangChain, LangGraph, AutoGen, ). Experience with vector databases (e.g., Pinecone, Milvus, Pgvector, ChromaDB). Strong understanding of distributed systems and microservices architecture. Proficiency in REST API development using FastAPI REST Framework or similar tools. Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes). Proven track record of deploying production-grade AI systems. Experience leading technical teams and fostering collaborative environments. Preferred/Bonus Points Advanced degree in Computer Science, AI, or related fields. Background in chemical and materials science applications. Contributions to open-source AI projects. Experience with fine-tuning and optimizing large language models (LLMs). Expertise in system monitoring and observability tools (e.g., Prometheus, Grafana). Experience with data engineering tools (e.g., Airflow, Delta Lake, Dask). Familiarity with Unix scripting and version control systems (e.g., Git). Prior experience working in AI/ML-focused environments using tools such as Ray, Torch, Kubeflow. Experience mentoring junior developers/engineers in best practices. 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.