Position: Machine Learning Engineering Manager
Location: MENA (Remote)
Employment Type: Part-Time
I'm looking for a talented Machine Learning Engineering Manager to join a client - a prestigious news agency known for its commitment to journalistic integrity and innovative solutions in fact-checking. This is a part-time and fully remote position.
The ideal candidate will be a fluent in Arabic with 10+ years of relevant tech experience, including prior people and tech project management. You should possess a solid understanding of machine learning concepts, coupled with a vision of how technology can accelerate fact-checking techniques in today’s fast-paced media landscape.
If you have the passion and expertise to lead a technical team in reshaping how media addresses misinformation and disinformation, we would love to hear from you!
Accountabilities
Machine Learning for Fact-Checking
- Algorithm Selection & Model Development: Lead the development and deployment of machine learning models tailored to fact-checking needs, focusing on detecting misinformation, disinformation, and logical fallacies.
- Solution Design: Design solutions that enhance the organization's ability to identify false news and provide accurate, timely fact checks.
- Innovation in Fact-Checking: Explore and implement cutting-edge machine learning approaches to improve the speed, accuracy, and effectiveness of fact-checking processes.
Data Management
- Data Strategy Development: Create and implement strategies for data collection, ensuring high-quality, relevant data is gathered for training and validating models.
- Data Analysis and Insights: Conduct thorough analyses of collected data to identify patterns, trends, and insights that can inform model development and fact-checking processes.
- Data Governance: Ensure that data management practices comply with ethical standards and regulations, maintaining data privacy and security while fostering trust in the organization’s use of technology.
Technology Strategy and Alignment
- Tech Roadmap Development: Create a technology roadmap that integrates AI/ML tools into the core fact-checking processes, aligned with the organization’s vision and goals.
- Strategic Innovation: Propose and develop new ideas and tools that can reshape how the organization tackles misinformation, contributing to long-term strategic growth.
- Business Integration: Ensure that technological solutions are seamlessly integrated with editorial workflows, supporting journalists with automated tools and data-driven insights.
Leadership and Cross-Functional Collaboration
- Team Leadership: Mentor and guide junior developers or technical staff, ensuring high team performance and growth.
- Lead problem-solving and troubleshooting machine learning and technology-related issues.
- Cross-Departmental Collaboration: Work closely with journalists and non-technical staff to ensure that machine learning solutions address the real-world challenges faced in fact-checking.
- Communication with leadership: Present technical insights in an accessible way to leadership, aligning machine learning developments with the broader organizational goals and strategy.
- Promote a culture of continuous learning and improvement, encouraging team members to stay updated with the latest machine learning trends and tools.
Project Management and Delivery
- End-to-end Development: Lead the implementation of machine learning and AI projects, from conception through to deployment and iteration.
- Milestone Tracking: Oversight timelines, milestones, and technical deliverables to ensure successful project completion.
- Prioritize tasks and allocate resources effectively, balancing immediate needs with long-term goals.
Monitoring and Adaptation
- Performance Evaluation: Regularly monitor the performance of deployed models to ensure they continue to meet the evolving needs of the organization in detecting misinformation.
- Adaptation: Adapt models to address new challenges in the fact-checking space, ensuring they remain relevant and effective as false news tactics evolve.
- Technical Oversight and Quality Assurance: Ensure that machine learning models are rigorously tested, validated, and meet the highest standards of quality.
- Maintenance: Maintain robust, scalable, and maintainable codebases that support future development and improvements.
Business Model and Vision Development
- Strategic Growth: Identify potential external applications and business viability of the ML solution, develop a business model that aligns with the organization’s mission and extend beyond current internal needs, and participate in pitching to potential investors, partners and funders.
- Cross-Sector Integration: Explore and recommend how machine learning solutions can integrate with broader media technologies to enhance the organization’s competitive edge.
Market Awareness and Trends
- Industry Insight: Stay informed on industry trends, particularly in fact-checking, media technology, and AI/ML developments.
- Adaptability: Regularly assess how external trends can be leveraged to keep the organization at the cutting edge of technology.
- Solution Integration: Identify and integrate third-party tools or solutions that can accelerate or enhance the fact-checking process.
Ethics and Compliance
- Bias and Fairness: Ensure that machine learning models are unbiased and align with journalistic standards for accuracy, integrity, and fairness.
- Compliance: Stay updated with data privacy and ethical AI regulations, ensuring all machine learning tools are compliant with industry standards and policies.
- Integrity: Implement best practices for data handling, model training, and algorithm development to ensure compliance and ethical use of technology.
Reporting and Documentation
- Reporting: Regularly report project progress and performance metrics to leadership, highlighting key metrics, successes, and areas for improvement.
- Documentation: Maintain clear and comprehensive documentation of all machine learning processes, ensuring transparency and knowledge-sharing across the team.
- Needs Assessment: Advocate for the technical needs of the organization, providing insight into the resources, tools, and infrastructure required for success.
Qualifications:
Language Proficiency:
- Fluent in Arabic with strong proficiency in English
Experience:
- 10+ years of experience in software development and machine learning.
- 3+ years in a leadership or management role, with experience managing technical teams.
- Previous experience in machine learning models tailored to fact-checking and the capturing and analyzing of news is highly preferred.
Technical Skills:
- Proven experience in developing and deploying machine learning models (at least 3-5 completed projects).
- Proficiency in programming languages such as Python, R or similar, with experience using machine learning frameworks (e.g., TensorFlow, PyTorch).
- Familiarity with data management techniques and tools for data collection and preprocessing (e.g., SQL, Pandas).
- Fact-Checking Awareness: Strong understanding of fact-checking challenges, false news, basic logical fallacies, along with their implications for media production and consumption.
- Project Management: Experience leading cross-functional projects with a track record of delivering on time and within scope (e.g., completed projects within set deadlines).
- Collaboration Skills: Demonstrated ability to work with teams across different functions (e.g., collaboration on at least 2 cross-departmental projects).
- Ethical Awareness: Understanding of data privacy regulations and ethical standards in AI usage (e.g., compliance with GDPR).
