Wizard

Benefits:

• Competitive compensation packages, including equity

• Remote work culture

• Health, dental and vision insurance

• Mental healthcare support and services

• 401(K) plan

Education Requirements:

• MS/Ph.D. in computer science, mathematics, or another quantitative discipline or exceptional work experience in ML engineering

Experience Requirements:

• 1-3 years ML Experience

• Experience developing machine learning-driven products at scale

• Experience solving problems using Machine Learning with PyTorch

• Exceptional understanding of designing ML systems in production at scale and applying a quantitative approach to identifying performance bottlenecks and cost/performance tradeoffs

• Practical experience with large data systems, data models, and batch and streaming data pipelines

Other Requirements:

• Ability to ramp up quickly on our tech stack, which includes GCP, Kubernetes, Airflow, Pandas, PyTorch, Python, and Node.js

• Extensive experience with model deployment, both static within applications and dynamic, using Flask, FastAPI, or similar frameworks

Responsibilities:

• Build and maintain a training pipeline for fine-tuning open-source LLMs

• Serve and Deploy the LLM on GPUs

• Formulate novel product issues to ML problems and propose and iterate on solutions quickly

• Triage customer issues to model/system shortcomings and communicate the complex architecture in a simple, comprehensible way to internal stakeholders

• Keep up with the fast-growing generative AI space and incorporate the latest developments into our system

Benefits:

• Competitive compensation packages, including equity

• Remote work culture

• Health, dental and vision insurance

• Mental healthcare support and services

• 401(K) plan

Education Requirements:

• Ph.D.in Computer Science, Mathematics, or another quantitative field or outstanding professional experience in machine learning engineering

Experience Requirements:

• Over 5 years of industry experience in machine learning, with a proven track record of developing ML-driven products at scale

• Experience building recommender systems, preferably in the e-commerce space

• Demonstrated expertise in tackling complex problems using machine learning frameworks like PyTorch

• A comprehensive understanding of ML system design in production environments, including a quantitative approach to evaluating performance bottlenecks and cost/performance trade-offs

• Hands-on experience with large-scale data systems, sophisticated data models, and batch and streaming data pipelines

Other Requirements:

• Quick adaptability to our technology stack encompasses GCP, Kubernetes, Airflow, Pandas, PyTorch, Python, and Node.js

• Deep experience in model deployment strategies, utilizing both in-application static methods and dynamic approaches with frameworks like Flask, FastAPI, or equivalents

Responsibilities:

• Architect and oversee development for novel product issues to ML problems and propose and iterate on solutions quickly

• Triage customer issues to model/system shortcomings and communicate the complex architecture in a simple, comprehensible way to internal stakeholders

• Build and maintain pipelines for fine-tuning open-source LLMs

• Serve and Deploy the LLM on GPUs

• Working with Product and Engineering counterparts, translate customer needs and business requirements into long term vision and roadmap at Wizard

Benefits:

• Competitive compensation packages, including equity

• Remote work culture

• Health, dental and vision insurance

• Mental healthcare support and services

• 401(K) plan

Education Requirements:

• MS/Ph.D. in computer science, mathematics, or another quantitative discipline or exceptional work experience in ML engineering

Experience Requirements:

• 3+ years ML Experience

• Experience developing machine learning-driven products at scale

• Experience solving problems using Machine Learning with PyTorch

• Exceptional understanding of designing ML systems in production at scale and applying a quantitative approach to identifying performance bottlenecks and cost/performance tradeoffs

• Practical experience with large data systems, data models, and batch and streaming data pipelines

Other Requirements:

• Ability to ramp up quickly on our tech stack, which includes GCP, Kubernetes, Airflow, Pandas, PyTorch, Python, and Node.js

• Extensive experience with model deployment, both static within applications and dynamic, using Flask, FastAPI, or similar frameworks

Responsibilities:

• Build and maintain a training pipeline for fine-tuning open-source LLMs

• Serve and Deploy the LLM on GPUs

• Formulate novel product issues to ML problems and propose and iterate on solutions quickly

• Triage customer issues to model/system shortcomings and communicate the complex architecture in a simple, comprehensible way to internal stakeholders

• Keep up with the fast-growing generative AI space and incorporate the latest developments into our system