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Perform other job-related duties as assigned. 3+ years of experience, depending on education, as an ML, backend, data, or platform engineer developing and working with large scale, complex systems. 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. 1+ year of experience for Senior designing and developing online and production grade ML systems. A degree in computer science, engineering, or a related field. Strong programming skills: Proficiency in languages like Python, Go, or Java. System design & architecture: Ability to design scalable and efficient ML systems. Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. ML knowledge: A basic understanding of ML algorithms, techniques, and best practices. 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Experience in building, automating, and enhancing tools and frameworks for the data platform to streamline pipeline development. Experience modeling data sets for different types of sources and business processes. Experienced with many or all of the following technologies: Python, Go, Scala, Java, Kubernetes, Docker, Terraform, CircleCi, GitHub Actions, Kafka, PubSub, Dataflow, Looker Experience in utilizing cloud environments such as GCP, AWS, or Azure. Experience with databases and Big Data tooling such as Redshift, BigQuery, Databricks, dbt. Strong written and verbal communication skills. Exposure to ML Ops or other data engineering workloads. Effective analytical, troubleshooting, and problem-solving skills. 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Design, build, and maintain global, highly available, low-latency infrastructure to service the needs of our customers. Partner internally with teams to improve performance, scalability, reliability and observability of our infrastructure. Empathetically help teams improve their systems' performance, scalability, reliability, and observability. Has a keen focus on performance and reliability of infrastructure and takes ownership of solving problems as they arise. Leave a trail of high-quality technical documentation of initiatives, postmortems, and endeavor to uphold good knowledge-sharing practices. Impart your domain expertise theoretically and practically. Join the Platform Engineering on-call rotation. Prior experience in implementing, configuring and optimizing Kubernetes and Terraform at scale. Experience in building and troubleshooting large-scale distributed production infrastructure in AWS or GCP. Demonstrated proficiency in working on CI/CD pipelines. Experience building global multi-region infrastructure, services, databases and storage. Experience in on call rotation and responding to incidents. 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Internal Communications: Drive internal communications initiatives and messaging to keep employees informed and engaged. Stakeholder Collaboration: Build and manage relationships with internal stakeholders and agency partners. Communications & Messaging: Write engaging materials for internal and external communications, including key messaging and blog posts. Media Relations: Prepare and coach spokespeople, pitch stories to tech, business, and consumer outlets, cultivate relationships with key reporters, and handle inbound media requests. Strategic Measurement & Analysis: Evaluate the success of communications initiatives to continuously improve strategies. Proven Experience: 5-8 years in corporate communications or public relations, with a blend of in-house experience at a global tech company and agency experience. Versatile Communicator: Proven agility in managing both external announcements and internal communications, from strategy development to execution. 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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.. 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