Machine Learning Engineer

Machine Learning Engineer

Requirements

• 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 Kubernetes 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.
• 3+ 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 designing and developing online and production grade ML systems.
• A degree in computer science, engineering, or a related field.

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.

Preferred Qualifications

No preferred qualifications provided.