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Requirements • Strong Python programming skills with attention to clean, efficient, and scalable code, • Experience b...

Research Ai Engineer Remote

$84k - $120k* · Senior Fully remote

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Everyone is welcome at Handshake. We know diverse teams build better products and we are committed to creating an inc...

Strategy Non Tech Ai Lead

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AI Research Engineer Handshake AI

Handshake Anywhere

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$84k - $120k · Senior Full-time Posted about 2 months ago 2 min read

Requirements • Strong Python programming skills with attention to clean, efficient, and scalable code, • Experience building and operating large-scale systems for model post-training, specialized data processing, or benchmark evaluation, • Deep familiarity with PyTorch and modern post-training techniques (RLHF, constitutional AI, etc.), • A background in applied machine learning, model evaluation, or large-scale data quality assessment, • Experience with benchmark design, evaluation methodologies, and performance measurement frameworks, • Clear communication skills and a collaborative mindset for cross-functional research teams, • (Desirable) Experience optimizing deep learning models for performance (e.g., memory usage, training speed), • (Desirable) Interest in the societal and ethical impacts of AI technologies, • (Desirable) Contributions to open-source ML infrastructure or tools

What the job involves • We're looking for a Research Engineer to join our Handshake AI Research team, where you'll help shape what the next generation of AI models can achieve. This is a hands-on, high-impact role focused on post-training methodologies, specialized domain data verification, and creating cutting-edge LLM benchmarks that measure real-world impact, • As a Research Engineer, you'll bring deep technical skill, curiosity, and rigor to every stage of the research-to-deployment pipeline—whether it's designing robust distributed infrastructure for massive experiments, writing high-performance ML code, or developing benchmarks and evaluations that define the future of AI capabilities, • Design and implement post-training systems and methodologies in close partnership with research scientists and domain experts, • Build and maintain infrastructure that supports large-scale model training, specialized data processing, and benchmark evaluation, • Develop robust frameworks for verifying the quality and integrity of highly specialized domain datasets, • Create next-generation LLM benchmarks that push the boundaries of model evaluation and capabilities assessment, • Optimize performance across software and hardware layers to accelerate post-training experimentation and deployment, • Collaborate across disciplines to ensure rigorous validation of model improvements and benchmark reliability

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