At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether its medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable.
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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.
Key Responsibilities
Technical Leadership
- 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
People Management
- 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
Strategic Planning & Execution
- 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
Cross-functional Collaboration
- 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
Operational Excellence
- 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
Required Qualifications
Experience
- 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
Technical Skills
- 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
Leadership Competencies
- 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
Preferred Qualifications
- 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
What We Offer
- 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
