Senior Machine Learning Engineer

Who we are: Factored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. ​We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed worldwide, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.

We are looking for a Senior Machine Learning Engineer to join our team. You will drive the development of AI products for external clients and internal efforts, and participate in the development of top-notch AI systems.

At Factored we are building a company that we all hold as our own, every single one of us. We need your skills to help take this rocketship to new heights and help create new opportunities for us.  In return, you will be rewarded with an amazing team that supports you, rich culture, shared success and the flexibility to work– from the comfort of your home. #LI-Remote

What you will be doing:

• Lead the ideation, prototyping, and development of machine learning models for batch, real-time and near real-time inference scenarios.
• Develop end-to-end model development cycles from exploratory data analysis and model building to training and evaluation.
• Drive continuous performance improvements and iterate on the deployed models.
• Understand and gather insights from data and model performance and relate those to business KPIs.
• Communicate and explain complex processes to different business stakeholders.

What you must bring:

• Strong background in Machine Learning and Statistical Modeling, with at least 6+ years of experience creating models end-to-end.
• Proficiency with cloud environments, with a minimum of 2 years of experience in either GCP, AWS, or Azure.
• Proven track record of leading projects from end-to-end(research, POCs, modeling & deployment), with a minimum of 2 years of experience, overseeing tasks and collaborating with team members to achieve project goals.
• 4+ years of experience designing and developing online and production-grade machine learning systems.
• Demonstrated expertise in DevOps tooling, including Kubernetes and Terraform.
• Strong data engineering skills encompassing the handling of large datasets, data cleaning, preprocessing, storage, and the implementation of both batch and streaming pipelines. Familiarity with orchestrators such as Argo and Airflow is a plus.
• Comprehensive experience in system design and architecture, with a deep understanding of distributed computing for both learning and inference processes.
• Proven track record in establishing and utilizing ML infrastructure components.
• A master's or PhD in a relevant field, focusing on machine learning systems or data science.

Nice to have:

• Experience with real-time scenarios, low-latency systems, and data-intensive environments is a plus.
• An advanced degree in Computer Science or related fields is a plus.
• Experience setting and executing strategic software system migrations and refactorings.
• Familiarity with PyTorch, TensorFlow, knowledge distillation, and recommender systems.
• Contributions to ML system and operations research, open-source projects, and blogs.
• Familiarity with ML serving solutions like Ray, KubeFlow or W&B.