Machine Learning Engineer

• Implement and optimize LLM-based features including prompt engineering, fine-tuning, and RAG systems
• Design, develop, and train machine learning models for various business applications (classification, regression, NLP)
• Conduct rigorous model evaluation using appropriate metrics and cross-validation strategies
• Develop evaluation pipelines for both traditional ML models and LLM applications
• Create reproducible experimentation frameworks for model iteration and improvement
• Build ensemble methods and optimize model architectures for performance/accuracy trade-offs
• Deploy ML infrastructure on AWS (SageMaker, Bedrock, EC2)
• Develop cost-effective solutions for both traditional models and LLMs
• Use Docker and Terraform to manage ML environments
• Build real-time and batch inference systems for ML models
• Create data pipelines and feature stores for model training
• Set up vector databases and RAG systems for LLM applications
• Implement monitoring and alerts for models in production
• Manage both traditional ML workloads and LLM API integrations
• Collaborate with engineering teams to integrate ML features into products
• Participate in code reviews ensuring high-quality, maintainable ML code
• Contribute to technical documentation and knowledge sharing
• Support other engineers in understanding and using ML systems
• Partner with product teams to scope and deliver ML features

Senior Machine Learning Engineer

• Implement and optimize LLM-based features including prompt engineering, fine-tuning, and RAG systems
• Design, develop, and train machine learning models for various business applications (classification, regression, NLP)
• Conduct rigorous model evaluation using appropriate metrics and cross-validation strategies
• Develop evaluation pipelines for both traditional ML models and LLM applications
• Create reproducible experimentation frameworks for model iteration and improvement
• Build ensemble methods and optimize model architectures for performance/accuracy trade-offs
• Deploy ML infrastructure on AWS (SageMaker, Bedrock, EC2) and develop cost-effective solutions
• Use Docker and Terraform to manage ML environments
• Build real-time and batch inference systems for ML models
• Create data pipelines and feature stores for model training
• Set up vector databases and RAG systems for LLM applications
• Implement monitoring and alerts for models in production
• Manage both traditional ML workloads and LLM API integrations
• Stay current with ML advancements, especially in LLMs
• Collaborate with engineering teams to integrate ML features into products
• Participate in code reviews ensuring high-quality, maintainable ML code
• Contribute to technical documentation and knowledge sharing
• Support other engineers in understanding and using ML systems
• Partner with product teams to scope and deliver ML features

Product Marketing

• Own the end-to-end GTM (Go-To-Market) strategy and execution for new product launches, upgrades and sunsets
• Actively drive market positioning, packaging, value proposition, messaging, and other initiatives based on market research, competitive analysis, and customer feedback
• Co-own launch decisions and communication strategies with product managers, ensuring that all departments are coordinated, Marketing and Support teams are prepared, and the end-to-end customer experience is top notch
• Partner closely with product managers and other cross functional leads to influence the product roadmap
• Develop competitive positioning, provide insights, and talking points that can be leveraged by Marketing, Sales, and Support teams
• Be the marketing focal point and cross-departmental coordinator on high-visibility marketing projects and product launches
• Be the go-to for any and all aspects of your product - business metrics, financials, marketing performance etc
• Collaborate with the Growth and Content Marketing teams to develop the customer communication plan and lead-gen strategies supporting product releases
• Craft all internal and external marketing communications about product launch activities – working closely with Product Management, UX, Engineering, Sales, and Marketing teams
• Develop and implement customer awareness and product engagement strategies and tactics tied to product releases
• Ability to lead cross-functional teams and gain consensus in order to move projects forward
• Keep the product marketing calendar up to date as timelines shift, communicating and collaborating often with the broader marketing team to adjust strategies and tactics accordingly
• Distill benefits from technical product functionality, turning complex information into simple, compelling messaging
• Direct promotional ‘sizzle-reel’ style videos and training videos to highlight new features, functionalities, or workflows in and out of the product
• Possess a "roll up your sleeves" and "scrappy" attitude, leveraging AI tools to independently develop marketing tactics and solutions, such as videos and webinars, with minimal external support

Moz

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Product Marketing Manager