Applied ML Scientist
Applied ML Scientists design and optimize machine learning systems that solve concrete business or scientific problems, moving beyond theoretical research to ship models in production environments. They work at the intersection of modeling and systems engineering, combining cutting-edge techniques like fine-tuning, reinforcement learning, and synthetic data generation with practical constraints around latency, cost, and real-world data distribution. These roles typically sit within dedicated applied research or product teams at AI-native companies, collaborating closely with engineers and domain experts to translate customer requirements or product challenges into effective training pipelines and evaluation frameworks.
Skills
What companies are looking for in this role.
Designing and implementing machine learning models for production systems
Conducting applied research to solve real-world problems using AI and machine learning
Training, fine-tuning, and evaluating large-scale deep learning models
Developing generative AI models and systems including language and vision models
Building end-to-end machine learning pipelines from data to deployment
Designing experiments to validate novel AI techniques and measure their effectiveness
Analyzing and interpreting model performance through rigorous evaluation frameworks and metrics
Translating domain-specific requirements into well-defined machine learning problems
Writing high-quality, production-ready, and well-tested code
Working with large-scale real-world datasets across diverse domains and modalities
Identifying root causes of model failures and developing mitigation strategies
Conducting scaling experiments and optimizing training pipelines for large-scale models
Optimizing models for specific hardware constraints and computational efficiency
Monitoring and debugging machine learning systems in production environments
Creating synthetic data and leveraging domain-specific datasets for model improvement
Applying reinforcement learning techniques to improve model alignment and performance
Building and deploying agentic AI systems that automate complex multi-step workflows
Developing multimodal AI systems that integrate and reason over diverse data types
Applying foundation models and prompt engineering to downstream applications
Implementing retrieval and ranking systems for information discovery and semantic search
Collaborating with cross-functional teams including engineers, product managers, and domain experts
Communicating complex technical research findings to peers and non-technical stakeholders
Translating user feedback and business requirements into technical ML solutions
Publishing research findings and presenting at academic conferences
Mentoring and guiding other researchers and engineers on ML projects
Technology
The tools and technologies that define this role.
Open Jobs
28 open Applied ML Scientist jobs across 16 companies.
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