
Lead Machine Learning Engineer
- Atlanta, GA
- Permanent
- Full-time
- Accelerate ML development using AI tools for code generation, feature engineering, optimization, and validation
- Stay up to date with advancements in ML, AI, and emerging technologies
- Design, build, and maintain ML models, algorithms, and robust pipelines for data processing, training, and inference
- Optimize model performance, scalability, and reliability in production environments
- Collaborate cross-functionally to translate model insights into business value and communicate project updates
- Contribute to ML infrastructure improvements, best practices, and documentation
- Partner with engineering teams to integrate AI-enhanced models and establish automated monitoring frameworks.
- Establish AI governance practices including bias detection, interpretability, compliance monitoring, and responsible deployment.
- Mentor teams in AI adoption, share best practices, and promote responsible AI innovation culture.
- Lead AI transformation initiatives including tool evaluation, governance development, and strategic adoption planning.
- Analyzes complex data sets to solve real-world business and customer use cases.
- Performs end-to-end development of machine learning models
- May assist with or lead the development of industry whitepapers or other technical publications.
- Continuously evaluate AI processes for accuracy, efficiency, and business impact while staying current on emerging technologies.
- Design agentic workflows for autonomous training, data pipelines, and analytical problem solving appropriate to experience level.
- AI-Accelerated Model Development: Use GitHub Copilot, Claude Code for rapid ML prototyping, automated feature engineering, and intelligent hyperparameter optimization.
- Agentic ML Workflows: Understand and deploy (P4+) AWS AgentSquad, AWS Strands, LangChain agents for autonomous training pipelines, multi-step analysis, and collaborative research.
- AI-Enhanced Model Interpretation: Build on traditional frameworks (SHAP, LIME) with AI tools for enhanced stakeholder communication and automated insights.
- AI-Powered Research: Leverage manual/autonomous competitive intelligence and research acceleration tools for methodology discovery and algorithm innovation.
- Proficiency in AI development tools (GitHub Copilot, Claude, GPT-4) for ML development with ability to validate AI outputs for production readiness.
- Understanding of agentic frameworks (AWS AgentSquad, AWS Strands, LangChain, agent patterns) with progression from basic configuration to custom enterprise system design.
- Knowledge of AI ethics, responsible AI practices, and governance frameworks for business-critical ML deployment.
- Ability to leverage AI like Co-Pilot for technical communication to stakeholders and cross-functional collaboration.
- Commitment to continuous learning in AI-augmented data science and responsible AI use.
- Bachelor’s degree in a related discipline and 6 years’ experience in a related field; or a different combination, such as a master's degree and 4 years’ experience; a Ph.D. and 1 years’ experience in a related field; or 14 years’ experience in a related field with no degree
- Skilled in analytical thinking, consulting, requirements analysis, system and technology integration and technology savvy.
- Skilled in collaborating with intent, communicating with impact, developing trust, driving innovation and striving for excellence.
- Other duties as needed or required.
- Proven track record of leading innovative projects from concept to proof-of-concept
- Deep expertise in multiple ML domains and familiarity with emerging research areas
- Strong experience in technology evaluation, competitive analysis, and strategic planning
- Demonstrated success in knowledge sharing and thought leadership (publications, speaking, etc.)
- Experience building and leading high-performing research or innovation teams
- Excellent communication skills for technical and executive audiences
- Strong network within the ML research community
- Experience with research collaboration and partnership development
- Experience in corporate research labs, innovation teams, or technology consulting
- Track record of identifying and successfully implementing breakthrough technologies
- Background in technology transfer from research to business applications
- Strong presence in the ML community (conference speaking, open-source contributions, etc.)
- Knowledge of emerging areas such as LLMs, Agents, foundation models, multimodal AI, or quantum ML
- Foster a culture of experimentation, learning, and calculated risk-taking
- Drive consensus on research priorities while maintaining innovation velocity
- Develop talent through mentoring in both technical skills and research methodologies
- Communicate complex experimental results and strategic implications to all organizational levels
- Lead by example in intellectual curiosity, scientific rigor, and knowledge sharing
- Build bridges between cutting-edge research and practical business applications
- Establish the team as a recognized center of excellence in experimental ML