Develop and implement large-scale machine learning and foundation-model systems, collaborate with research teams to integrate technologies, maintain ML lifecycle (data preprocessing, pre-/post-training, evaluation), conduct design reviews and code reviews, debug system issues, contribute to documentation and research publications, and represent the institute at conferences.
About the Institute of Foundation Models
We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.
As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.
The Role
As a Machine Learning Engineer at the Institute of Foundation Models, your primary responsibility is to develop and implement innovative machine learning models that address real-world challenges, pushing the boundaries of artificial intelligence research. You will collaborate with cross-functional teams to deploy scalable solutions, contributing to MBZUAI’s mission of driving impactful AI discoveries and positioning the institution as a leader in the global AI research community. Your expertise will be key in enhancing the performance of large-scale machine learning models, while supporting the development of transformative AI tools that can influence industries worldwide.
Key Responsibilites
- Collaborate with Research teams to understand technologies, adapting and integrating them into codebase.
- Develop and implement systems to support the lifecycle of machine learning models, such as data preprocessing, pre-training, post-training, evaluation and so on, especially foundation models.
- Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
- Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Contribute to research papers and represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.
- Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
Academic Qualifications
- Minimum: Bachelor’s degree or equivalent practical experience.
- Preferred: Master's degree or PhD in Computer Science or related technical field.
Professional Experience - Minimum
- 3 years of experience in software engineering, including experience with Machine Learning (ML) models, ML infrastructure, Natural Language Processing or Computer Vision.
- 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree in an industry setting.
- 2 years of experience with data structures or algorithms in either an academic or industry setting.
- 2 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, or natural language processing.
- Excellent problem-solving and troubleshooting skills to address complex technical challenges.
- Effective communication and collaboration skills to work with cross functional teams.
Professional Experience - Preferred
- 2 years of experience with improving performance during large scale data processing
- Hands-on experience with LLM algorithms, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF).
- Excellent data analysis skills.
Visa Sponsorship
This position is eligible for visa sponsorship.
Benefits Include
*Comprehensive medical, dental, and vision benefits
*Bonus
*401K Plan
*Generous paid time off, sick leave and holidays
*Paid Parental Leave
*Employee Assistance Program
*Life insurance and disability
Similar Jobs
.jpg)
Marketing Tech • Mobile • Software
Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.
Top Skills:
CeleryCi/CdCloud InfrastructureFeature StoresIamInfrastructure As CodeKafkaKubernetesMl ObservabilityMlflowMongoDBNetworkingPythonRabbitMQRayRedisRuby On Rails
Fintech • Payments • Financial Services
Design, productionize, and operate machine learning models and rule-based decision systems for credit underwriting. Build scalable pipelines for feature engineering, training, validation, deployment, monitoring, and continuous improvement. Optimize model performance, collaborate with software, credit, product, risk, and data teams, and promote strong engineering and MLOps practices across production ML systems.
Top Skills:
AirflowArgo WorkflowsDockerFeature StoresGrafanaJavaKubernetesLightgbmMlflowPandasPrometheusPysparkPythonPyTorchTensorFlowTrino SqlXgboost
eCommerce • Information Technology • Sharing Economy • Software
Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.
Top Skills:
AirflowAmazon RedshiftBigQueryCi/CdData LakesDbtDockerGithub ActionsKafkaKubernetesLightgbmPythonPyTorchRest ApisScikit-LearnSnowflakeSQLTensorFlowXgboost
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
