The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Responsibilities
Formulate, develop, and evaluate new algorithmic and system-level approaches for end-to-end AI serving
Design and experimentally evaluate endpoint configuration and execution policies
Perform hardware- and kernel-aware optimization by collaborating with model, kernel, compiler, and hardware teams
Build and benchmark experimental prototypes and large-scale measurements
Publish research results, file patents, and contribute to open-source systems
Requirements
Doctorate in relevant field
Master's Degree in relevant field
Bachelor's Degree in relevant field
Nice to have: designing and optimizing efficient inference systems
Nice to have: deep knowledge of attention and KV-cache optimizations
Seeking Applied Scientists with solid systems-building skills to push the frontier of large-scale model post-training and adaptation using Microsoft's AI customization platform, Frontier Tuning.
Compensation
The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,800 - $219,200 per year.
Responsibilities
Implement methods to adapt foundation models for enterprise-specific tasks
Contribute to the post-training stack (RL/fine-tuning, architectural adaptation, inference-time steering, tooling)
Implement and evaluate adaptation approaches under real-world enterprise deployment constraints
Partner with research and engineering teams to translate product or customer requirements into scalable model adaptation solutions
Explore post-training techniques that improve domain specialization, tool use, planning, or agentic behaviors
Requirements
Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
Nice to have: Reinforcement learning or post-training methods
Technical deep dives and system audits of generative AI solutions, LLM pipelines, autonomous multi-agent systems, and classical Data Science products within Schwarz Group companies.
Responsibilities
Conduct technical audits of generative AI solutions, LLM pipelines, autonomous multi-agent systems, and classical Data Science products.
Analyze source code, system architectures, and prompt strategies focusing on security, performance, and robustness.
Design, program, and optimize proprietary AI-assisted analysis and evaluation frameworks.
Analyze complex business processes and AI-driven workflows using BI and Process Mining tools (e.g., Celonis).
Support audit projects domestically and internationally, advising management and development teams.
Requirements
Successfully completed degree in Computer Science, Data Science, Mathematics, or comparable technical qualification.
Design, prototype, implement, evaluate, and optimize systems to generate sports datasets and predictions with high accuracy and low latency.
Compensation
Base salary: starting at $160,000 base plus bonus potential
Responsibilities
Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency.
Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.
Build, test, deploy and maintain production systems.
Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.
Support maintenance and optimization of cloud-native EDW and ETL solutions.
Applying understanding of company products and services and introducing technology innovations with respect to functionality, performance, reliability, and scalability.
Designing, developing, and maintaining the machine learning infrastructure that powers the company.
Performing research and development of machine learning algorithms to expand product capabilities.
Participating in building new experiences, improving existing products, ensuring quality assurance test designs and contributing to test systems architecture discussions.
Requirements
Master’s degree in Computer Science, Engineering, Mathematics, or a related field
Lead the development of machine learning solutions that improve model performance and business outcomes, owning complex modeling problems from initial investigation through production implementation.
Locations (5)
Columbus, United States
Austin, United States
Bay Area, United States
New York City, United States
Canada
Compensation
United States | Remote - Anticipated Base Salary Range
$167,700—$231,800 USD
Canada | Remote - Anticipated Base Salary Range
$159,000—$193,000 CAD
Nice to have: Distributed or large-scale ML training systems
Lead end-to-end development of machine learning models, from problem formulation and experimentation through deployment and performance evaluation
Design and evaluate advanced modeling approaches to improve predictive accuracy, robustness, and operational effectiveness
Identify opportunities to improve existing models through feature development, experimentation, and rigorous statistical analysis
Partner with engineering, product, and business stakeholders to translate complex requirements into measurable machine learning objectives
Guide technical decisions, review scientific methodologies, and share expertise to improve the quality and effectiveness of the team's work
Requirements
Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
PhD in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field (preferred)
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