Senior Engineering Manager - AI

At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether it’s medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable.

Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity. 

We are seeking an experienced Senior Engineering Manager to lead our AI/ML engineering teams in building cutting-edge artificial intelligence solutions. This role requires a unique blend of technical expertise in AI/ML, proven engineering leadership, and strategic thinking to drive innovation at scale.

• Define and execute the technical strategy for AI/ML initiatives across multiple product areas
• Oversee the design and architecture of scalable ML systems, from data pipelines to model deployment
• Drive decisions on technology stack, frameworks, and infrastructure for AI/ML workloads
• Ensure engineering excellence through code reviews, design reviews, and technical mentorship
• Stay current with AI/ML research and industry trends to inform strategic decisions

• Lead, mentor, and grow a team of 15+ AI engineers, data scientists, and software engineers
• Build high-performing teams through hiring, performance management, and career development
• Foster a culture of innovation, collaboration, and continuous learning
• Conduct regular 1:1s, performance reviews, and career development conversations
• Champion diversity, equity, and inclusion initiatives within the engineering organization

• Partner with Product Management to define AI product roadmap and priorities
• Translate business objectives into technical initiatives and measurable outcomes
• Manage multiple concurrent AI/ML projects from conception to production deployment
• Establish and track KPIs for team performance, model quality, and system reliability
• Balance innovation with pragmatic delivery to meet business deadlines

• Work closely with Data Science, Product, Design, and other engineering teams
• Communicate technical concepts and trade-offs to non-technical stakeholders
• Represent engineering in executive discussions and strategic planning sessions
• Build relationships with external partners, vendors, and research institutions
• Drive alignment across teams on AI ethics, responsible AI practices, and governance

• Establish best practices for ML model development, testing, and deployment
• Implement MLOps practices for continuous integration and deployment of ML models
• Ensure compliance with data privacy regulations and AI governance policies
• Drive improvements in model monitoring, A/B testing, and experimentation frameworks
• Manage engineering budget and resource allocation

• 13+ years of software engineering experience, with 5+ years focused on ML/AI systems
• 5+ years of engineering management experience, including managing managers
• Proven track record of shipping ML products at scale in production environments
• Experience with full ML lifecycle: data collection, feature engineering, model training, deployment, and monitoring

• Deep understanding of machine learning algorithms, deep learning, and statistical methods
• Proficiency in ML frameworks (TensorFlow, PyTorch, JAX) and programming languages (Python, Scala, Java)
• Experience with distributed computing frameworks (Spark, Ray) and cloud platforms (AWS, GCP, Azure)
• Knowledge of MLOps tools and practices (Kubeflow, MLflow, Airflow, Docker, Kubernetes)
• Understanding of data engineering, ETL pipelines, and big data technologies

• Demonstrated ability to build and scale engineering teams
• Strong communication skills with ability to influence at all levels of the organization
• Experience driving technical strategy and making architectural decisions
• Track record of successful cross-functional collaboration and stakeholder management
• Ability to balance technical depth with business acumen

• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field
• Deep experience with Large Language Models (LLMs), Small Language Models (SLMs), and generative AI applications
• Expertise in building production AI agent systems:
• Multi-agent architectures and swarm intelligence
• Memory systems: short-term, long-term, episodic, and semantic memory
• Planning algorithms: hierarchical planning, goal decomposition, and backtracking
• Tool use and function calling optimization
• Agent communication protocols and coordination strategies
• Experience with advanced agent frameworks: DSPy, Guidance, LMQL, Outlines for constrained generation
• Knowledge of prompt engineering techniques: few-shot, chain-of-thought, self-consistency, constitutional AI
• Experience with RAG architectures: vector stores, hybrid search, re-ranking, and query optimization
• Expertise in training techniques: supervised fine-tuning, RLHF, DPO, PPO, constitutional AI, and synthetic data generation
• Experience with parameter-efficient fine-tuning methods: LoRA, QLoRA, prefix tuning, and adapter layers
• Knowledge of model optimization techniques: quantization (INT8, INT4), distillation, pruning, and flash attention
• Extensive experience in dataset curation for LLM training:
• Web-scale data processing (Common Crawl, C4, RefinedWeb methodologies)
• Creating instruction-tuning datasets (Alpaca, Dolly, FLAN-style formats)
• Building preference datasets for RLHF/DPO training
• Domain adaptation and specialized corpus creation
• Multi-lingual and code dataset preparation
• Knowledge of data mixing strategies, replay buffers, and curriculum learning for optimal training
• Experience with data augmentation techniques: paraphrasing, back-translation, and synthetic data generation using LLMs
• Expertise in data decontamination and benchmark pollution prevention
• Experience with workflow automation platforms: n8n, Zapier, Make for business process automation
• Knowledge of enterprise integration patterns: event-driven architectures, saga patterns, and CQRS
• Strong background in data science: statistical analysis, A/B testing, experimentation design, and causal inference
• Experience with data mesh architectures and building self-serve data platforms
• Expertise in data quality frameworks, data contracts, and SLA management for data pipelines
• Experience with vector databases (Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, FAISS) and embedding systems
• Knowledge of privacy-preserving ML techniques: differential privacy, federated learning, secure multi-party computation
• Background in specific AI domains: NLP, Computer Vision, Recommendation Systems, or Reinforcement Learning
• Experience with LLM evaluation frameworks and benchmarking (HELM, EleutherAI eval harness, BigBench)
• Hands-on experience with popular LLM frameworks: Hugging Face Transformers, vLLM, TGI, Ollama, LiteLLM
• Experience with dataset processing tools: Datasets library, Apache Beam, Spark NLP
• Publications or contributions to open-source ML projects
• Experience in high-growth technology companies or AI-first organizations
• Knowledge of AI safety, ethics, and responsible AI practices
• Experience with multi-modal models and vision-language models

• Opportunity to work on cutting-edge AI technology with real-world impact
• Competitive compensation package including equity
• Access to state-of-the-art computing resources and research tools
• Budget for conferences, training, and professional development
• Collaborative environment with talented engineers and researchers
• Flexible work arrangements and comprehensive benefits

Who we are:
FourKites®, the leader in AI-driven supply chain transformation for global enterprises and pioneer of advanced real-time visibility, turns supply chain data into automated action. FourKites’ Intelligent Control Tower™ breaks down enterprise silos by creating a real-time digital twin of orders, shipments, inventory and assets. This comprehensive view, combined with AI-powered digital workers, enables companies to prevent disruptions, automate routine tasks, and optimize performance across their supply chain. FourKites processes over 3.2 million supply chain events daily — from purchase orders to final delivery — helping 1,600+ global brands prevent disruptions, make faster decisions and move from reactive tracking to proactive supply chain orchestration.

Working at FourKites

We provide competitive compensation with stock options, outstanding benefits and a collaborative culture for all employees around the globe, including: