Lead ML AI Engineer

• Lead the design and implementation of representation learning systems, embedding models, vector search solutions, and retrieval-augmented architectures.
• Define technical architecture for AI-first platforms utilizing foundation models, large language models, autonomous agents, and classical machine learning techniques.
• Establish best practices for agentic AI systems including orchestration, memory management, tool integration, confidence calibration, and human-in-the-loop workflows.
• Architect multimodal AI solutions that combine imagery, text, and structured data for detection, classification, reasoning, and decision support.
• Evaluate emerging AI technologies and guide adoption decisions across the organization.
• Translate complex business and technical challenges into scalable, explainable, responsible AI solutions.
• Define success metrics and partner with leadership to ensure measurable business impact.
• Mentor engineers and data scientists, helping elevate technical excellence across the team.
• Contribute to innovation through intellectual property development, research publications, and technical thought leadership.

Master's degree or Ph.D. in Computer Science, AI/ML, Mathematics, or a related field.

• Candidates with a Master's should have 7+ years of experience, including demonstrated technical leadership.
• Ph.D. holders may have 6+ years of relevant experience, including demonstrated technical leadership.
• Proven track record of leading the design and delivery of production ML systems end to end, and setting technical direction that other engineers build on.
• Deep expertise in representation learning and embeddings, vector similarity search, and retrieval-augmented approaches — able to architect the foundation-model core, not just contribute to it.
• Strong command of ML fundamentals, including regression, classification, forecasting, and model interpretability.
• Hands-on experience with deep learning architectures such as CNN, LSTM, GRU, GANs, BERT, Diffusion Models, and Transformers.
• Substantial experience with LLMs (e.g., GPT-3/4, Claude, Mistral) and architecting agent-based solutions using frameworks like LangGraph, CrewAI, or AutoGen.
• Multimodal / vision-language model experience — jointly encoding imagery with text or structured data — at an architectural level.
• Proficiency in Python; familiarity with other programming languages like C++ or Java is a plus.
• Fluency with ML frameworks such as PyTorch, TensorFlow, scikit-learn, and experience working with large datasets and distributed systems.
• Experience with geospatial or spatial data — spatial indexing, geospatial data structures, or entity resolution / conflation — is a strong advantage, as the work centers on spatial reasoning at scale.
• A track record of making independent architectural decisions, defending them with evidence, and holding a high measurement bar for whether systems actually work.
• Demonstrated ability to mentor senior engineers and lead cross-functional, agile teams.
• Curiosity and exposure to adjacent technologies like TinyML, Quantum Computing, or 3D modeling/AR is an advantage.
• Excellent problem-solving and communication skills, including the ability to represent complex technical work to senior stakeholders.