Manager, Machine Learning & AI Engineering

Company description

Spark Foundry is a global media agency that exists to bring HEAT – Higher Engagement, Affinity, and Transactions – to brands. By combining flawless media fundamentals with aggressive innovation, Spark inspires consumers to pay more attention, to care more about our clients’ brands, and to buy more products and services from them.
Balancing the nimble spirit of a startup with the powerhouse soul of Publicis Media, Spark Foundry delivers the best of both worlds to a client roster that spans some of the world’s best and most beloved brands and companies. We combine boutique-caliber insights and service with the buying clout and first-look access of a global leader, bringing the heat to challenger brands that want to act like giants, and to giant brands that want to act like challengers.
With a bottom-up culture that celebrates diversity and aims for all voices to be heard, Spark has become a magnet for the industry’s best talent, with one of the best retention rates in the industry. And by applying a whole-person approach to professional and personal development, Spark develops a workforce that is well prepared for today’s challenges, and also poised to create meaningful careers in the years to come.
Because we know that heat arises the intersection of complementary forces, our professionals come from myriad disciplines and backgrounds: data, analytics, and insights, content and creative production, communications and strategy, finance and marketing, and sociology, psychology, and other liberal arts disciplines.

As an ML/AI engineer, you'll develop and deploy both GenAI applications and traditional ML systems that help media planners, strategists, and analysts work smarter. You'll build models that forecast campaign performance, LLM-powered tools that generate insights, and recommendation systems that optimize media strategies. This is a hands-on role where you'll own projects end-to-end and see your work used daily by agency teams.

• Design, build, and deploy machine learning and GenAI solutions for media use cases
• Develop time-series forecasting, classification, clustering, and recommendation models, taking them from experimentation through production deployment
• Build LLM-powered applications such as RAG systems, conversational analytics, insight generators, and prompt-based tools for content and creative workflows
• Integrate and operationalize foundation models (e.g., OpenAI, Anthropic Claude, etc.), including prompt design, vector search, semantic retrieval, and output optimization
• Own ML features end-to-end from requirements gathering and experimentation to production deployment, monitoring, and iteration
• Write production-quality, scalable code with proper testing, documentation, performance optimization, and error handling
• Monitor deployed models for accuracy, drift, reliability, and business impact; retrain and improve models based on real-world usage
• Experiment rapidly, evaluate tradeoffs across accuracy, cost, latency, and complexity, and validate solutions with real users
• Collaborate closely with data engineers and agency teams to align data pipelines, technical solutions, and business needs
• Communicate technical concepts clearly to non-technical stakeholders and contribute to documentation, reviews, and knowledge sharing
• Stay current on ML and GenAI advancements relevant to media, evaluate new tools pragmatically, and bring informed recommendations to the team

• 3-5+ years of hands-on experience building and deploying ML models in production
• Strong Python skills and experience with ML frameworks (scikit-learn, PyTorch, Tensor Flow, XGBoost, Light
  
  GBM)
• Practical experience building applications with LLMs (OpenAI, Anthropic, or open-source models)
• Solid understanding of ML fundamentals: feature engineering, evaluation, overfitting, imbalanced data
• Experience deploying models beyond notebooks (APIs, batch jobs, real-time inference)
• Strong SQL skills and experience working with large datasets
• Experience with cloud platforms (AWS, GCP, or Azure) and Docker
• Software engineering fundamentals:
  Git, testing, CI/CD, code reviews
• Ability to work independently, make…