Machine Learning Engineer

Title: Machine Learning Engineer
Only W2 candidates (No visa restrictions)
Locations: US remote
Team Role
This applied research team is dedicated to developing intelligent, end-to-end solutions that empower our advertiser sellers to achieve their advertising goals. This involves providing strategic guidance, such as recommending optimal ad placements, selecting the right inventory, keywords, bids, and budgets for their campaigns, and facilitating continuous optimization. This rapidly evolving space presents significant business potential and is a critical area of need. To address this, we build scalable data and Machine Learning/Deep Learning services, which entails working with massive datasets, applying diverse data science and ML/DL techniques, and adhering to the best engineering practices.

Responsibilities
·Work with Applied Researchers, Engineers, Analytics and multi-functional teams to produce end-to-end production-ready solutions.
·Design, implement, and maintain data and ML/DL services, encompassing efficient data pipeline, ML model training, inference & deployment processes, robust tracking and monitoring system, as well as other MLOps work.
·Optimize software performance to achieve the required throughput and / or latency.
·Analyze data, interpret experiments, and uncover trends, insights, and opportunities. Translate complex data into actionable recommendations for technical and non-technical audiences.
·Monitor, triage and drive resolution processes for production system incidents, ensuring system reliability.
Requirements
·BS or MS in Computer Science or equivalent experience
·Strong programming skills in SQL, Python, Scala or Java
·Solid understanding of machine learning fundamentals and applications.
·Proficiency with key machine learning and deep learning libraries, e.g. PyTorch/Tensorflow, Transformers, scikit-learn, vLLM, and Ray.
·Strong debugging, analytical, and communication skills to collaborate effectively with engineers and researchers.
·Good understanding of ML/DL infrastructure, including areas like autoscaling, job scheduling, and workload orchestration across heterogeneous compute (CPU/GPU/accelerators).
·Experience with big data technologies (i.e., Spark, Hadoop) and database technologies (i.e., SQL, NoSQL)
·Experience in solving problems using data science, building practical solutions, and deploying models into production environments.
·Experience with observability stacks (e.g. Prometheus, Grafana) and data visualization tools (e.g., Tableau, Kibana) is a plus.