Machine Learning Engineer

Whalar Group•New York, NY

61d•$150,000 - $160,000•Onsite

About The Position

We’re looking for a Machine Learning Engineer (Level 2) 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.

Requirements

2+ 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.

Responsibilities

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.