Staff ML Engineer

Overview In this Staff ML Engineer role, you define roadmaps for generative content and lead end-to-end delivery from design to launch. You apply AI to deliver hyper-personalized, brand-consistent content at scale. You’ll scale training and inference pipelines, fine-tuning, and model evaluation, while collaborating with cross-functional teams to ship impactful solutions. This role helps shape the future of enterprise marketing through cutting-edge Generative AI. You’ll work with top AI leaders to drive growth and innovation. Compensation / Benefits Competitive salary, equity, and 401(k)Medical, dental, and vision insuranceHSA and FSA options Flexible time off including parental leave Well-being programs and mental/physical health support Daily lunch & snacks Responsibilities Define the roadmap for generation of content types (text, image, audio, video) for brand-specific, personalized content Own delivery of key business, product, and technology initiatives across design, specification, development, testing, and launch Establish and implement processes for model training, fine-tuning, testing, and operating Generative AI models at scale Investigate, prototype, and deliver innovative ML/AI solutions Collaborate with engineers, product, and business teams to identify product improvements and growth opportunities Establish data-driven development culture by defining, tracking, and operationalizing metrics at feature, product, and company level Key requirements 10+ years of experience designing, building, and scaling ML systems for training, testing, and running models at scale Master’s or PhD in Computer Science or related field Experience building and evolving ML training/inference systems at scale for real-world problems Proficiency in Python, C/C++ (or equivalent)Ability to create self-contained, reusable, testable modules and strong prototyping mindset Experience with AzureML, GCP Vertex, AWS Sage Maker or similar platforms Collaborative across disciplines Proactive prototyping and experimentation Data-driven decision makingML training and inference at scale Generative AI model deployment and tuning Cloud ML platforms (AzureML, Vertex AI, Sage Maker)