About the Role:

The Dating Outcomes group is responsible for making sure people get to see their most compatible matches as well as helping them better present themselves or even start a conversation when they match with someone. In short we help people go on dates! We are hiring senior/staff ML practitioners to help us build the foundations of an AI first dating experience using the latest advancements in the field leveraging Hinge’s years worth of preference data. You can expect to work on recommendation systems end to end, experiment with using LLMs, photo and mixed input embedding models as well as building and deploying real time predictive models that directly impact millions of users' experience. This is a fast-growing team and you will get a chance to own and define the strategy, vision, and plan for how to accelerate machine learning at Hinge.

Responsibilities

• Own and contribute to foundational models (e.g. CLIP embeddings) that powers our recommendations pipelines.
• Contribute to the research and development of recommender models as well experiment with the latest ML innovations (e.g. LLM agents and transcription models)
• Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to our daters incrementally.
• Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process.
• Mentor and educate ML Engineers on current and up and coming research, technologies and best practices of doing ML at scale.
• Perform other job-related duties as assigned.

What We're Looking For

• Strong programming skills: Proficiency in languages like Python, Java or C++
• System design & architecture: Proven track record of training and deploying large scale ML models specially DNNs. Good understanding of distributed computing for learning and inference.
• Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow or W&B is a plus.
• ML knowledge: Deep understanding of DNN architectures, track record of building, debugging and fine tuning models. Familiarity with PyTorch, TF, knowledge distillation, recommender systems are a plus.
• Dev-ops skills: The ability to establish, manage, and use data and compute infrastructure such as Kubenetes and Terraform.
• Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Deep understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow.
• Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds..
• Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation
• Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes.
• Experience:
  • 4+ years of experience, depending on education, as an MLE.
  • 2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes
  • 1+ year of experience leading projects with at least 1 other team member through completion.
  • 2+ years of experience for Senior designing and developing online and production grade ML systems.
• Education: A degree in computer science, engineering, or a related field.