AI Research Scientist, Ads Ranking

Meta•Sunnyvale, CA

1d•$219,000 - $301,000

About The Position

Meta is seeking a Research Scientist to join our Ads Ranking organization. Individuals in this role are expected to be recognized experts in areas such as artificial intelligence, machine learning, and computational statistics, particularly including areas such as: sequential decision making, graph learning, reinforcement learning, deep learning, data representation and theory, and optimization. The ideal candidate will have an interest in producing new science to understand intelligence and technology to make computers smarter, and an equal interest in taking new research findings in this area and implementing them towards product needs.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
12+ years of experience in machine learning research, including developing and publishing novel approaches in areas such as optimization, deep learning, representation learning, or related core ML domains
Track record of identifying and solving foundational machine learning problems that have resulted in measurable impact on large-scale AI systems or products
Experience defining multi-year research strategies and influencing technical direction across multiple teams or organizations
Experience translating theoretical machine learning research into production systems, including designing experiments, defining evaluation metrics, and connecting research outcomes to organizational priorities
Experience communicating complex machine learning concepts and research findings to both technical and non-technical audiences through publications, design documents, or technical presentations

Nice To Haves

Demonstrated ability to develop new debugging, evaluation, or verification methodologies that generalize across model families and prevent systemic failure modes
Experience leading cross-organizational research programs in large-scale model training, efficient inference, or foundational model architectures
Experience collaborating with applied research and product engineering teams to bring core ML advances from research prototype to global-scale deployment
History of publishing influential research in top-tier machine learning venues such as NeurIPS, ICML, ICLR, or equivalent, with demonstrated impact on the broader research community

Responsibilities

Identify and lead research on the most challenging and high-impact open problems in core machine learning, including areas such as optimization, generalization, representation learning, and scalable training
Develop novel machine learning algorithms, architectures, and theoretical frameworks that advance the state of the art and address previously intractable problems
Translate fundamental research findings into production-grade systems that deliver measurable improvements to Meta's AI-powered products and infrastructure
Define and drive the long-term technical vision and research roadmap for a core ML domain, gaining alignment across multiple research and engineering organizations
Establish extensible technical foundations, modeling standards, and evaluation frameworks that promote consistency and quality across research teams and product areas
Identify systemic gaps in existing ML approaches and develop invariants, benchmarks, and methodologies that prevent entire classes of modeling failures
Collaborate with research engineers, product teams, and infrastructure organizations to ensure research advances are operationalized at scale with reliability and efficiency
Communicate complex research findings through publications, technical reports, and presentations that serve as long-term reference points for the broader ML community
Mentor other researchers and engineers across the organization, providing customized technical guidance and fostering rigorous and innovative research practices
Monitor developments in the academic and industry ML landscape to identify emerging techniques and assess their relevance and risk to Meta's research strategy