What you will do

Senior Machine Learning Engineer (Level 3)

Foam, part of Whalar Group, is the operating system for managing digital talent. Foam is a suite of intuitive pitching tools and AI-enhanced features powered by real-time, certified metrics from Instagram, TikTok, YouTube, and Snap. Foam empowers managers with the data they need to analyze content performance, inform talent negotiations, and maximize brand opportunities. With over 40,000 Creators and hundreds of millions of integrated data points, Foam streamlines the entire pitching process, eliminating spreadsheets, screenshots, and slides, allowing managers to focus on providing strategic support to their talent. Learn more at foam.io.

About the Role:
We’re looking for a Senior Machine Learning Engineer (Level 3) to join our growing ML Engineering team and lead the development of advanced AI systems that power our platform. You’ll design and deploy the core intelligence behind our products, with a focus on building autonomous, stateful AI agents capable of reasoning, learning, and acting in dynamic environments.

In this role, you’ll bridge the gap between research and production—architecting, training, and scaling systems that turn cutting-edge ideas into reliable, production-ready tools. You’ll collaborate with engineering, data, and product teams to create cohesive, high-performance ML ecosystems across multimodal search, forecasting, and video understanding.

If you’re passionate about pushing the boundaries of applied AI—designing intelligent systems that think and evolve—this is an opportunity to have real impact.

Here’s What You’ll Do Day to Day:
* Design and deploy autonomous AI agents, including reasoning loops, memory layers, and orchestration pipelines.
* Build observability and evaluation systems to monitor reasoning, token usage, and model performance, ensuring reliable production behavior.
* Lead the development of multimodal ML pipelines for semantic search, RAG, recommendation systems, and vector search across text, image, and video data.
* Engineer high-throughput time-series analytics and forecasting models that connect batch OLAP queries with real-time inference.
* Develop and maintain scalable asynchronous APIs and containerized services, ensuring reliability, monitoring, and performance optimization.
* Partner with product and engineering teams to translate business goals into measurable ML outcomes.
* Drive research-to-production pipelines for experimental AI projects and evaluate emerging technologies to advance our platform.

What we offer

The perks:

Foam provides flexible benefits and collaborative work environments/experiences, so employees can work productively in a setting that best and uniquely suits their needs.

Estonia Benefits
* 25 days of PTO + Sick days + Winter break
* Monthly phone/internet reimbursement
* Professional development stipend
* New joiner Home office allowance
* 16 weeks of paid maternity/paternity leave
* Volunteer days
* Social programs

Requirements

Here’s What We’re Looking For:

• 4+ years of experience in machine learning engineering, building production-grade ML systems.
• Hands-on experience with agentic AI frameworks (e.g., LangGraph, LlamaIndex, Zep, Mem0, Langfuse, LangSmith).
• Experience building RAG pipelines, recommendation systems, and/or vector search applications (e.g., Pinecone, Vespa, PostgreSQL + pgvector).
• Strong background in time-series modeling, anomaly detection, and large-scale data analysis (e.g., Clickhouse).
• Skilled in asynchronous API design, containerization, and modern CI/CD workflows (FastAPI, Docker, Kubernetes, GitHub/Bitbucket).
• Excellent EDA skills with the ability to translate data insights into production-ready ML solutions.
• Comfortable working with LLM ambiguity, designing systems that fail gracefully and learn continuously.
• Proactive, independent, and curious—able to own complex features end-to-end and raise the technical bar for the team.
• Strong communication skills—able to explain trade-offs between AI approaches and align technical metrics to business goals.
• Experience leveraging AI tools and functionality to improve workflow efficiency, research, and experimentation.