Wiraa
Machine Learning Engineer
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About The Company
At Scale, our mission is to develop reliable and impactful AI systems that support the world's most critical decisions. We specialize in providing high-quality data, advanced full-stack technologies, and innovative solutions that empower enterprises and governments to build, deploy, and oversee AI applications with confidence. Our collaborations include industry leaders such as Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and various U.S. government agencies including the Army and Air Force. As part of our ongoing expansion, we are committed to accelerating the development of AI-powered solutions that deliver tangible results and foster technological advancement.
About The Role
Applied Intelligence Systems (AIS), a key division within the Scale Generative AI Platform (SGP), is focused on pioneering agentic applications across diverse enterprise and government use cases. We develop the infrastructure and tooling necessary to power agentic AI systems in production environments, complemented by applied machine learning research, design, and evaluation to ensure these systems operate reliably at scale. The AIS team encompasses multiple workstreams, including agent evaluation and oversight, orchestration and tool-use infrastructure, model and systems optimization, and exploratory research into new agent capabilities. The Machine Learning Engineer role is pivotal within this context, owning critical components such as context and memory capabilities, ensuring their correctness, performance, and robust evaluation.
We seek a highly skilled Machine Learning Engineer capable of managing complex technical challenges from research and prototyping to full-scale deployment. This role involves working with knowledge bases, vector stores, retrieval-augmented generation (RAG) pipelines, and context engines to build intelligent agents that generate meaningful impact for enterprise clients. The ideal candidate will possess a strong background in AI systems, excellent engineering fundamentals, and the ability to operate effectively in ambiguous, fast-paced environments.
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Qualifications
- 5+ years of experience in building and deploying machine learning or AI systems in production environments
- Master’s or PhD degree in Computer Science, Machine Learning, AI, or a related field, or equivalent practical experience
- Deep expertise in retrieval systems, RAG, embeddings, vector indexing, knowledge representation, and semantic search
- Proficiency in Python, with a track record of writing production-quality, maintainable, and testable code
- Strong understanding of knowledge graphs, ontologies, and structured reasoning over enterprise data
- Experience designing and implementing RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking
- Ability to develop and maintain integrations between ML components, enterprise data sources, APIs, and vector databases
- Experience building scalable backend services and data pipelines supporting ML and large language models in production
- Excellent communication skills, with the ability to collaborate effectively across cross-functional teams
- Experience working in fast-growth startup environments and scaling AI products
Responsibilities
- Own large sections of the AI platform end-to-end, from initial design and research to deployment and maintenance
- Design, develop, and optimize knowledge representation systems such as ontologies and knowledge graphs to facilitate structured reasoning
- Create and refine RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking processes
- Build and maintain integrations between retrieval systems, ML models, enterprise data sources, APIs, and vector databases
- Develop context retrieval systems that effectively balance recall, precision, latency, and operational costs
- Design and implement evaluation frameworks, datasets, and metrics to assess retrieval quality, context relevance, and overall agent performance
- Build reliable backend services and data pipelines that support ML workflows and large language models in production
- Conduct experiments to test new capabilities, ensuring high quality and rapid feedback cycles with customers
- Collaborate with product, ML, and infrastructure teams to influence platform development and strategic direction
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Benefits
- Competitive salary and performance-based bonuses
- Comprehensive health, dental, and vision insurance coverage
- Flexible working hours and remote work options
- Generous paid time off and holiday leave
- Opportunities for professional development and continuous learning
- Inclusive and diverse workplace culture that values innovation and collaboration
- Support for work-life balance and wellness initiatives
Equal Opportunity
At Scale, we are committed to fostering an inclusive environment where everyone can thrive. We are an equal opportunity employer and do not discriminate based on race, color, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. We also provide reasonable accommodations to applicants with disabilities throughout the recruitment process. We believe that diversity enhances our innovation and success, and we welcome applicants from all backgrounds to join our team.
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