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Job title: AI Data Engineering and Data Science Manager (USA REMOTE) Company: Turnitin Job description: Company Description 100% REMOTE MUST BE U.S. BASED Turnitin, an AI-focused leader in the educational and research sectors, has been at the forefront of promoting academic integrity and innovation for over two decades. We are renowned for our cutting-edge solutions which have been embraced by thousands of academic institutions, corporations, and publishers worldwide. At Turnitin, we believe in flexibility, offering remote working as a standard arrangement. We value diversity, respect local cultures, and honor individual choices. For those who prefer, we provide the option to work in our offices located in various parts of the globe, including Oakland, Dallas, Pittsburgh, Kyiv (Ukraine), Newcastle (UK), and Utrecht (Netherlands). Our team is diverse yet unified by a shared commitment to making a significant impact in education. AI and data science are integral to our success and ambitious product roadmap. Joining us as an AI Data Engineering and Data Science Manager, you'll become part of a global team of proactive, supportive, and independent professionals committed to delivering sophisticated, well-structured AI and data systems. You'll closely collaborate with different teams within Turnitin to integrate AI and data science across a broad suite of products, designed to enhance learning, teaching, and academic integrity. Job Description In your role as AI Data Engineering and Data Science Manager, you'll be expected to be versatile and well-balanced in your skills, covering a range of responsibilities: Leadership: Build and grow a team of AI data engineers and data scientists, ensuring their growth and high performance. AI Data Engineering: Design, build, operate and deploy real-time data pipelines at scale using AI methods and best practices. Apply cutting edge data warehousing, data science and data engineering technologies to accelerate Turnitin's AI R&D efforts . Automate low-value tasks and enable faster time to market and better reusability of new AI initiatives. Collaboration: Partner with AI R&D teams and the Data Platform Team in collecting, creating, curating and maintaining high-quality AI datasets. Ensure alignment and integration of data architecture and data models across different products and platforms. Strategic Contribution: Serve as a thought leader in AI and data science, advising Turnitin's senior leadership on how to leverage AI-driven data engineering to create future-proof data strategies and activate data to build a hybrid data engineering platform. Hands-on Involvement: Engage in data engineering and data science tasks as required to support the team. Lead external data collection efforts - including state of the art prompt engineering techniques - to support the construction of state of the art AI models. Innovation: Drive data innovation through research and development to unearth insights from Turnitin's rich data resources. Communication: Ensure clarity of the company's vision and mission across the team and foster excellent communication within the organization. Continuous Learning: Stay updated on new tools and development strategies, and bring innovation recommendations to leadership. Qualifications At least 5 years of experience in data engineering and data science, ideally focused on enabling and accelerating AI R&D. At least 2 years of experience in a managerial or technical leadership role, with responsibility covering large crossfunctional projects. Strong proficiency in Python, Java and SQL. Strong Proficiency with Redshift, Hadoop, Elasticsearch and cloud platforms (AWS, Azure, GCP). Familiarity interacting with AI frameworks including PyTorch and TensorFlow and AI libraries such as Huggingface and Scikit-Learn. Expertise in interacting with and prompting cutting-edge Large Language Models (LLMs) and LLM APIs. Strong problem-solving, analytical, and communication skills, and the ability to thrive in a fast-paced, collaborative environment. Desired Qualifications 7+ years of experience in data engineering and data science, with a focus on AI and machine learning projects. 4+ years of experience in a managerial or technical leadership role. Familiarity with natural language processing (NLP) techniques and tools. Experience in the education or education technology sectors. Experience with data visualization and data communications. Characteristics for Success As an AI Data Engineering and Data Science Manager, you should have: A passion for creatively solving complex data problems. The ability to work collaboratively and cross-functionally, with strong leadership skills. A strong growth mindset and ability to constantly improve your skills and knowledge. Excellent track record of delivering results and ensuring a high level of quality. A desire to mentor, guide and grow high potential individual contributors. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.