Description :

About the job

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.

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