At The ReWork Group, we partner with high-growth startups and forward-thinking companies to build the future. Partnering with one of our growing Series-A clients, we're on the search for a
Machine Learning Engineer
who wants to transform advertising through the power of creators, data, and AI.
With the rise of influencer marketing, they're hundreds (if not thousands) ways to make influencer accessibility as measurable, predictable, and scalable as traditional paid advertising. This is your opportunity to build it at scale.
Currently building a next-generation platform that connects influencers using AI-powered matchmaking, campaign automation, and generative tools, this is the new standard in Influencer Marketing.
If you're a Machine Learning Engineer who loves building, optimizing, and deploying ML systems at scale, and you're excited to push the boundaries of AI, this might be your next role.
What You'll Bring
- Proven experience designing, building, and maintaining production-grade, end-to-end machine learning systems.
- Deep expertise in ML algorithms, embeddings, retrieval systems, and evaluation methodologies.
- Hands-on experience with LLMs, including fine-tuning, inference optimization, and agent frameworks.
- Strong background in ML infrastructure: feature stores, vector databases, model serving, and real-time inference pipelines.
- Advanced Python proficiency and experience with PyTorch, TensorFlow, FastAPI, NumPy, scikit-learn, and data processing frameworks.
- Experience building scalable data pipelines (batch and streaming) using tools like Spark, Kafka, or equivalent technologies.
Who You Are
- You write clean, reusable, well-documented code for ML pipelines and distributed systems.
- You love experimenting with cutting-edge AI frameworks, ML optimizations, and deployment techniques and you move quickly from ideas to working prototypes.
- You collaborate effectively with engineering, product, and data teams to ship ML-powered features that enhance user experience.
- You stay curious about advances in ML infrastructure, LLM optimization, retrieval-augmented systems, and real-time personalization.