4+ years of experience in building and deploying machine learning APIs in production environments, preferably cloud-based., Strong foundation in deep learning modeling with experience in NLP, computer vision, or information retrieval., Proficiency in Python, Pytorch, and Hugging Face transformers library., A Master's or PhD in Machine Learning, Computer Science, Mathematics, Statistics, or equivalent industry experience..
Key responsabilities:
Develop and train deep learning models for NLP-related tasks using pre-trained and custom transformers.
Build and maintain systems, APIs, and data pipelines to deliver deep learning insights across products.
Collaborate with deep learning engineers, MLOps engineers, and product managers to drive projects from inception to production.
Suggest and lead organization-wide initiatives while contributing to the technical vision and strategy.
Report This Job
Help us maintain the quality of our job listings. If you find any issues with this job post, please let us know.
Select the reason you're reporting this job:
Numerator is a data and tech company bringing speed and scale to market research. Numerator blends first-party data from over 1 million US households with advanced technology to provide unparalleled 360-degree consumer understanding for the market research industry that has been slow to change. Headquartered in Chicago, IL, Numerator has more than 2,000 employees worldwide. The majority of Fortune 100 companies are Numerator clients.
We’re reinventing the market research industry. Let’s reinvent it together.
At Numerator, we believe tomorrow’s success starts with today’s market intelligence. We empower the world’s leading brands and retailers with unmatched insights into consumer behavior and the influencers that drive it.
Numerator is looking for a passionate Sr. Deep Learning Software 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:
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.
Build and maintain systems, APIs, and end-to-end data pipelines to deliver deep learning insights throughout all of Numerator’s products and platforms.
Work closely with other deep learning 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.
Have the freedom to suggest and drive organization-wide initiatives while being part of providing the technical vision and strategy at Numerator.
What You'll Bring to Numerator
4+ years experience building and deploying robust machine learning APIs in production environments (ideally cloud-based environments such as AWS or GCP).
Strong background in the foundations of deep learning modeling with a proven track record of building high throughput, production quality deep learning pipelines for NLP, computer vision, information extraction/retrieval, or related practice
Demonstrable fluency in Python, Pytorch, and Hugging Faces transformers library
Knowledge in the latest NLP-related algorithms and methods such as LLMs, transformers, sequence-to-sequence models, word and sentence embeddings, attention, information retrival etc
Experienced Software Engineering, Data Modeling, and debugging/profiling fundamentals
A Masters or PhD in Machine learning, Computer Science, Mathematics, Statistics, or another quantitative discipline or 5+ years equivalent industry experience
Nice to Haves:
You are familiar with monitoring and deployment tools and platforms, such as Docker, Kubernetes, and AWS Services.
Production experience with LLMs including RAG, Agenic patterns, and information retrieval techniques. LLM Self-hosting and training experience not required.
Demonstrated ability to drive selection of machine learning approaches to solve specific problems coupled with the ability to clearly communicate tradeoffs
Experience with one or more model inference optimization libraries (TensorRT, ONNX, torch script, etc)
General software design patterns (REST, MVC, Auto-scaling, etc.)
Experience supporting machine learning solutions for multiple languages
What We Offer
An inclusive and collaborative company culture - we work in an open environment while working together to get things done, and adapt to the changing needs as they come.
An opportunity to have an impact in a technologically data driven company.
Ownership over data and environments in an industry leading product.
Market competitive total compensation package.
Volunteer time off and charitable donation matching.
Strong support for career growth, including mentorship programs, leadership training, access to conferences and employee resources groups.
Regular hackathons to build your own projects and Engineering Lunch and Learns.
Great benefits package including health/vision/dental, unlimited PTO, flexible schedule, 401K/RRSPs matching, travel reimbursement, and more.
If this sounds like something you would like to be part of, we'd love for you to apply! Don't worry if you think that you don't meet all the qualifications here. The tools, technology, and methodologies we use are constantly changing and we value talent and interest over specific experience.
Required profile
Experience
Spoken language(s):
English
Check out the description to know which languages are mandatory.