Senior Engineering Manager - AI - Remote Eligible

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.

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

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