Principal/Senior Staff Machine Learning Engineer/Applied Scientist

This is a hybrid role based in our Palo Alto or San Francisco offices and will require you to be in the office on Tuesdays and Thursdays.

What’s so interesting about this role?

At Grindr, our mission is to show a user the right match, in the right place, at the right time. We tailor our dating platform for a diverse and global audience, so customers worldwide with different tastes and backgrounds can find their best match on every visit. We grow our relationships with our users by leveraging our deep understanding of them to provide relevant and timely recommendations. We believe the current customer experience is only scratching the surface of innovative experiences that are made possible as dating continues to shift online, and that science is at the center of delivering a step-function change for our CX.

We are looking for a highly motivated and experienced Machine Learning Engineer to join our team and help us shape the future of machine learning at Grindr. In this role, you will tackle new challenges in machine learning that will have a real impact on the way people discover and interact with the world around them in the LGBT community. You will collaborate with a world-class team of research scientists and engineers to develop new machine learning algorithms, systems, and applications that will bring step-function impact to the business metrics.

What’s the job? As a Principal ML engineer you will have deep subject matter expertise in the area of recommendations science. You will work with multiple teams of engineers and product managers to translate business and functional requirements into concrete deliverables. You will lead ground-breaking efforts in Generative AI to develop new approaches to Personalization, and provide thought leadership to scientists and engineers to invent and implement scalable ML recommendations supporting new Customer Experiences. 

• Lead cutting-edge algorithm development in machine learning and collaborate with other engineering teams to adopt the innovations into Grindr problems
• Collect, analyze, and synthesize findings from data and build intelligent data-driven model
• Scope and independently solve moderately complex problems; write clean, efficient, and sustainable code
• Be an expert in matching algorithm and recommender systems machine learning models and own the implementation of dating recommendations in Grindr
• Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across dating, personalization, growth, discovery, ads, and search

What we’ll love about you     

• BS in Computer science/Math or related field.
• Mastery of at least one systems language (Python, Java, C++) and one DL framework (Pytorch, Tensorflow, Keras) and one data analysis library (Pandas, R)
• Extensive experience building, deploying, and maintaining end-to-end recommendation systems 
• Experience in building complex, real-time systems involving AI, ML, NLP, Recommender Systems, Ads with successful delivery to customers.
• Computer Science fundamentals in data structures, algorithm design, and complexity analysis.
• 5+ years working experience in engineering or R&D teams that build large-scale production-level ML-driven projects. Ability to take a project from requirements gathering and design to actual product launch
• Strong track record of working with language modeling technologies. This could include the following: Developing generative and embedding techniques, modern model architectures, fine-tuning / pre-training datasets, and evaluation benchmarks.
• Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment and expertise with Git version control.

We’ll really swoon if you have    

• MS or PHD degree in Computer Science or Machine Learning.
• Experience working on recommender systems and matching algorithms in dating industry
• Expertise in large language models or demonstrated ability to develop this expertise quickly.
• Exposure to architectural patterns of large-scale software applications
• Experience in shipping products to millions of customers or have started a new line of product
• Experience with Hadoop/HBase/Pig or MapReduce/Sawzall/Bigtable/Hive/Spark
• Demonstrated track record of peer-reviewed scientific publications that advance the state-of-the-art for applied science.