Numerator is looking for a passionate Machine Learning Research Engineer to join our growing Machine Learning team. This is a unique opportunity where you will get a chance to work with an established and rapidly evolving platform that handles millions of requests and massive amounts of events, and other data. In this position, you will be responsible for taking on new initiatives to design, build, deploy, and support high performance deep learning systems in a rapidly-scaling environment.
As a member of our team, you will make an immediate impact as you help build out and expand our technology platforms across several software products. This is a high growth and impact role that will give you tons of opportunity to drive decisions for projects from inception through production.
What You'll Do:
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Develop and train deep learning models on computing clusters to perform NLP-related tasks, such as applying both pre-trained and custom transformers for NER, sequence classification, language modeling, etc.
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Develop LLM driven solutions using fine-tuning, RAG, and agentic patterns
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Build and maintain systems, APIs, and end-to-end data pipelines to deliver deep learning insights throughout all of Numerator’s products and platforms.
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Work closely with other Machine Learning Research Engineers, MLOps engineers, product managers, and other teams, both internal and external stakeholders, owning a large part of the process from problem understanding to shipping the solution.
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Have the freedom to suggest and drive organization-wide initiatives while being part of providing the technical vision and strategy at Numerator.
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4+ years experience building and deploying robust machine learning APIs in production environments (ideally cloud-based environments such as AWS or GCP).
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Background in the foundations of deep learning modeling with experience building high throughput, production quality deep learning pipelines for NLP, computer vision, information extraction/retrieval, or related practice
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Production experience with LLMs including RAG, Evals, Agentic patterns, and information retrieval techniques.
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Foundational understanding of Python, Pytorch, and Hugging Face transformers library
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Knowledge in the latest NLP-related algorithms and methods such as LLMs, transformers, sequence-to-sequence models, word and sentence embeddings, attention, information retrieval, etc
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Experienced software engineering, data modeling, and debugging/profiling fundamentals
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A Masters or PhD in Machine learning, Computer Science, Mathematics, Statistics, or another quantitative discipline or 5+ years equivalent industry experience
Nice to Haves:
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Demonstrated ability to drive selection of machine learning approaches to solve specific problems coupled with the ability to clearly communicate tradeoffs
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Experience with one or more model inference optimization libraries (TensorRT, ONNX, torch script, vLLM, etc)
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General software design patterns (REST, MVC, Auto-scaling, etc.)
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Experience supporting machine learning solutions for multiple languages
Top Skills
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