Launch Potato

Operations

• Own and optimize Launch Potato’s messaging systems across email, SMS, and push channels to increase reliability, scalability, deliverability, and business impact
• Own and scale ESP, SMS, and push notification platforms; lead technical vendor relationships and platform configurations
• Drive development of lifecycle, transactional, and promotional messaging workflows that increase engagement and retention
• Lead major data and platform migrations while maintaining compliance and robust documentation standards
• Monitor and resolve deliverability, domain, and IP issues across all messaging systems; maintain high inbox performance
• Build lightweight automation tools and scripts to increase operational efficiency and reduce manual workflows
• Lead root cause analysis and incident resolution related to messaging infrastructure
• Enable internal stakeholders (especially marketing) through training, documentation, and scalable workflows
• Collaborate cross-functionally with engineering, analytics, and product to align messaging execution with company KPIs

Messaging Operations and Platform Lead

Technical Messaging Operations Lead

Sales

• Grow Launch Potato’s revenue by developing, closing, and onboarding high-value media partnerships that drive measurable performance marketing outcomes for our clients.
• Generate a consistent flow of qualified outbound leads via multi-channel prospecting (email, LinkedIn, Slack communities).
• Conduct at least 12 discovery calls per month to identify prospects’ growth objectives and align LP’s solutions.
• Close new partnerships to reach combined annual revenue earned of $250,000 or more by month 6.
• Maintain 100% CRM hygiene and forecast accuracy in Pipedrive with weekly updates to pipeline status, stage, and deal probability.
• Complete 100% of new client handoffs to Account Management within 5 days of closing.
• Test and iterate on 2+ new outbound strategies or verticals per quarter.

Advertiser Sales Manager

Business Development Rep

• Grow Launch Potato’s revenue by developing, closing, and onboarding high-value media partnerships that drive measurable performance marketing outcomes for clients.
• Generate a consistent flow of qualified outbound leads via multi-channel prospecting (email, LinkedIn, Slack communities) within first 30 days.
• Conduct at least 12 discovery calls per month identifying prospects’ growth objectives and align LP’s solutions accordingly.
• Close a set number of new partnerships per quarter, achieving combined annual revenue of $250,000+ by month 6.
• Maintain 100% CRM hygiene and forecast accuracy in Pipedrive with weekly updates.
• Complete new client handoffs to Account Management within 5 days of closing.
• Test and iterate on 2+ new outbound strategies or verticals per quarter to expand partner footprint.

Business Development Manager

Machine Learning Engineer

• Build and deploy ML models serving 100M+ predictions daily
• Develop ranking algorithms that balance relevance, diversity, and revenue
• Implement real-time personalization features with <50ms latency requirements
• Run A/B tests and analyze model performance against business KPIs
• Optimize models for production (latency, throughput, cost)
• Collaborate with product teams to identify new personalization opportunities

Senior Machine Learning Engineer

• Lead a team of 3-5 ML engineers building personalization systems
• Architect multi-stage ranking systems (retrieval → ranking → re-ranking)
• Define technical roadmap aligned with business objectives
• Drive improvements in model quality, system reliability, and development velocity
• Establish best practices for model development and deployment
• Partner with product, data science, and engineering teams
• Contribute hands-on to critical model development (50% coding)

Lead Machine Learning Engineer

• Drive business growth by building and optimizing recommendation systems that personalize experience for millions of users daily
• Own modeling, feature engineering, data pipelines, and experimentation for personalization
• Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
• Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
• Design ranking algorithms that balance relevance, diversity, and revenue
• Deliver real-time personalization with latency <50ms across key product surfaces
• Run statistically rigorous A/B tests to measure true business impact
• Optimize for latency, throughput, and cost efficiency in production
• Partner with product, engineering, and analytics to launch high-impact personalization features
• Implement monitoring systems and maintain clear ownership for model reliability

Lead Machine Learning Engineer, Recommendation Systems

• Build ML software and infrastructure that drives ad performance and user engagement at scale
• Develop and scale advertising algorithms that optimize engagement and revenue
• Apply cutting-edge ML approaches (e.g., reinforcement learning, deep learning for ranking) to improve ad performance
• Build scalable infrastructure to support real-time ad decisioning across millions of requests per day
• Conduct rigorous experiments (A/B and multivariate tests) to measure model impact on business KPIs
• Continuously improve ETL pipelines and monitoring systems to ensure reliable ad delivery
• Collaborate with global teams across product, data, and engineering to launch high-impact ad features
• Stay ahead of emerging technologies and integrate them into our ML stack when they create competitive advantage

Lead Machine Learning Engineer, Ad Performance

• Drive business growth by building and optimizing recommendation systems that personalize experience for millions of users daily
• Own modeling, feature engineering, data pipelines, and experimentation for personalization
• Build and deploy ML models serving 100M+ predictions per day
• Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
• Design ranking algorithms that balance relevance, diversity, and revenue
• Deliver real-time personalization with latency <50ms across key product surfaces
• Run statistically rigorous A/B tests to measure true business impact
• Optimize for latency, throughput, and cost efficiency in production
• Partner with product, engineering, and analytics to launch high-impact personalization features
• Implement monitoring systems and maintain model reliability ownership

Lead ML Engineer, Recommendation Systems

• Elevate Launch Potato’s advertising performance by building scalable, production-ready ML systems that optimize revenue while enhancing user experience
• Design, deploy, and refine algorithms that improve ad targeting, relevance, and efficiency at global scale
• Develop and scale advertising algorithms that optimize engagement and revenue simultaneously
• Apply cutting-edge ML approaches (e.g., reinforcement learning, deep learning for ranking) to improve ad performance
• Build scalable infrastructure to support real-time ad decisioning across millions of requests per day
• Conduct rigorous experiments (A/B and multivariate tests) to measure model impact on business KPIs
• Continuously improve ETL pipelines and monitoring systems to ensure reliable ad delivery
• Collaborate with global teams across product, data, and engineering to launch high-impact ad features
• Stay ahead of emerging technologies and integrate them into our ML stack when they create competitive advantage

Lead ML Engineer, Ad Performance

• Design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys
• Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
• Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
• Design ranking algorithms that balance relevance, diversity, and revenue
• Deliver real-time personalization with latency <50ms across key product surfaces
• Run statistically rigorous A/B tests to measure true business impact
• Optimize for latency, throughput, and cost efficiency in production
• Partner with product, engineering, and analytics to launch high-impact personalization features
• Implement monitoring systems and maintain clear ownership for model reliability

• Design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys
• Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
• Enhance data processing pipelines (Spark, Beam, Dask) for efficiency and reliability
• Design ranking algorithms that balance relevance, diversity, and revenue
• Deliver real-time personalization with latency <50ms across key product surfaces
• Run statistically rigorous A/B tests to measure true business impact
• Optimize for latency, throughput, and cost efficiency in production
• Partner with product, engineering, and analytics teams to launch personalization features
• Implement monitoring systems and maintain ownership for model reliability
• Own modeling, feature engineering, data pipelines, and experimentation workflows

Senior Machine Learning Engineer, Recommendation Systems

• Elevate Launch Potato’s advertising performance by building scalable, production-ready ML systems that optimize revenue while enhancing user experience.
• Develop and scale advertising algorithms that optimize engagement and revenue simultaneously
• Apply cutting-edge ML approaches (e.g., reinforcement learning, deep learning for ranking) to improve ad performance
• Build scalable infrastructure to support real-time ad decisioning across millions of requests per day
• Conduct rigorous experiments (A/B and multivariate tests) to measure model impact on business KPIs
• Continuously improve ETL pipelines and monitoring systems to ensure reliable ad delivery
• Collaborate with global teams across product, data, and engineering to launch high-impact ad features
• Stay ahead of emerging technologies and integrate them into our ML stack when they create competitive advantage

Senior Machine Learning Engineer, Ad Performance

• Drive business growth by building and optimizing recommendation systems that personalize experience for millions of users daily
• Own modeling, feature engineering, data pipelines, and experimentation for personalization
• Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
• Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
• Design ranking algorithms that balance relevance, diversity, and revenue
• Deliver real-time personalization with latency <50ms across key product surfaces
• Run statistically rigorous A/B tests to measure business impact
• Optimize for latency, throughput, and cost efficiency in production
• Partner with product, engineering, and analytics teams to launch personalization features
• Implement monitoring systems and maintain ownership for model reliability

• Establish the technical vision for personalization at Launch Potato, solving the company’s most complex ML challenges and influencing strategy, architecture, and innovation across teams
• Define company-wide personalization strategy and architecture, driving alignment across all ML teams
• Solve critical technical challenges such as cold start, real-time learning, and exploration/exploitation tradeoffs
• Design and implement advanced ML solutions using cutting-edge techniques (e.g., graph neural networks, causal models, bandit algorithms)
• Create and enforce ML architecture patterns, design standards, and reusable infrastructure across teams
• Lead multi-quarter, cross-functional initiatives that redefine how personalization impacts business KPIs
• Act as technical mentor to senior ML engineers, guiding complex decision-making and scaling team capability
• Represent Launch Potato’s technical brand externally through speaking engagements, open source contributions, or publications
• Champion privacy-preserving personalization, responsible AI practices, and adaptive learning systems

Principal ML Engineer, Recommendation Systems

• Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.
• Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
• Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
• Design ranking algorithms that balance relevance, diversity, and revenue
• Deliver real-time personalization with latency <50ms across key product surfaces
• Run statistically rigorous A/B tests to measure true business impact
• Optimize for latency, throughput, and cost efficiency in production
• Partner with product, engineering, and analytics to launch high-impact personalization features
• Implement monitoring systems and maintain clear ownership for model reliability

Senior ML Engineer, Recommendation Systems

• Build scalable, production-ready ML systems that optimize revenue while enhancing user experience
• Design, deploy, and refine algorithms that improve ad targeting, relevance, and efficiency at global scale
• Develop and scale advertising algorithms that optimize engagement and revenue simultaneously
• Apply cutting-edge ML approaches (e.g., reinforcement learning, deep learning for ranking) to improve ad performance
• Build scalable infrastructure to support real-time ad decisioning across millions of requests per day
• Conduct rigorous experiments (A/B and multivariate tests) to measure model impact on business KPIs
• Continuously improve ETL pipelines and monitoring systems to ensure reliable ad delivery
• Collaborate with global teams across product, data, and engineering to launch high-impact ad features
• Stay ahead of emerging technologies and integrate them into our ML stack when they create competitive advantage

Senior ML Engineer, Ad Performance

Principal Machine Learning Engineer, Recommendation Systems

• Launch Potato is a profitable digital media company reaching 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState
• Mission to connect consumers with leading brands through data-driven content and technology
• Remote-first team headquartered in South Florida and spanning over 15 countries
• Role: Technical visionary for personalization and optimization systems as Principal ML Engineer
• Design company-wide personalization architecture and strategy
• Solve complex technical challenges (cold start, exploration/exploitation, real-time learning)
• Research and implement state-of-the-art ML techniques
• Define standards and patterns used across all ML teams
• Lead cross-functional initiatives spanning multiple quarters
• Mentor senior engineers and review critical technical decisions
• Represent the company in the external ML community