Senior ML/AI Engineer

Beast Industries is a multifaceted media and entertainment company founded by Jimmy Donaldson, popularly known as MrBeast, the most watched person in the world. Renowned for revolutionizing digital content creation, Beast Industries encompasses a diverse portfolio of ventures that extend far beyond its origins on YouTube. With a mission to entertain, inspire, and create significant social impact, Beast Industries operates across various domains including digital media, philanthropy, consumer products, and innovative business initiatives. At Beast Industries, we believe in the transformative power of digital media and its potential to entertain, educate, and effect positive change. Our commitment to innovation, creativity, and philanthropy drives us to explore new frontiers, create unforgettable experiences, and build a legacy that inspires future generations.

Senior ML/AI Engineer

Primary: Bay Area (San Francisco/Peninsula) | Secondary: NYC

We are looking for a senior ML/AI Engineer to help us build and scale intelligent systems that power product experiences, internal tools and business workflows. This role sits at the intersection of machine learning, software engineering, and modern AI application development, with a focus on taking ideas from prototype to reliable production systems.

You will work across both traditional ML systems and AI/LLM-powered applications, helping design, build, deploy, and maintain scalable solutions that deliver measurable value. This is a strong fit for someone who enjoys combining solid engineering fundamentals with practical experience in ML, Deep learning, LLMs and production infrastructure.

• →Design, build and maintain production-grade machine learning and AI systems.
• →Develop and deploy ML models, inference services and end-to-end pipelines.
• →Build AI-powered application workflows, including LLM-based features, retrieval and ranking pipelines and other intelligent services.
• →Partner closely with software engineers, product teams and data scientists to bring ML and AI capabilities into production.
• →Create evaluation, monitoring, and observability frameworks to measure system quality, reliability, and business impact.
• →Improve performance across latency, throughput, scalability and cost
• →Build safeguards and validation layers to improve output quality, reliability, and trustworthiness.
• →Contribute to architecture decisions and best practices across ML and AI systems.
• →Troubleshoot production issues across model pipelines, data flows and AI services.
• →Mentor junior engineers and help raise the bar for technical execution across the team

• Relevant engineering experience: 5+ years of experience in machine learning engineering, AI engineering, or a related technical field.
• Creative and strategic problem-solver: Able to break down complex challenges, identify practical opportunities, and develop innovative solutions that drive measurable impact.
• Strong ownership mindset: Takes accountability for projects, decisions and outcomes from inception through execution and delivery.
• Continuous learner: Actively seeks new knowledge, tools and techniques to deepen expertise, stay ahead of industry trends and expand overall impact.
• Highly collaborative: Communicates clearly and works effectively across technical, product, business and cross-functional teams.

• Experience building LLM-powered applications, including prompt engineering, RAG pipelines, embeddings, vector databases, and structured outputs
• Strong understanding of machine learning fundamentals, including evaluation, experimentation, feature engineering and performance tradeoffs
• Familiarity with orchestration tools such as Airflow or Kubeflow
• Experience with real-time or event-driven systems such as Kafka or Flink
• Familiarity with cloud platforms such as AWS or GCP
• Familiarity with evaluation methods for AI systems, including benchmark design, human review workflows and error analysis
• Familiarity with AI safety, guardrails, monitoring, and responsible deployment practices

In this role, success means building ML and AI systems that are not only technically strong, but also reliable, scalable, and valuable in production. Over time, you will help improve the speed, quality, and maturity of how we develop and operate intelligent systems, while enabling new product capabilities powered by machine learning and modern AI.