Senior Engineering Manager at FourKites

Sydney, New South Wales, Australia -

Full Time

Start Date

Immediate

Expiry Date

12 Feb, 26

Salary

0.0

Posted On

14 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI/ML, Technical Leadership, People Management, Strategic Planning, Cross-Functional Collaboration, Operational Excellence, Machine Learning, Deep Learning, MLOps, Data Engineering, Cloud Platforms, Programming Languages, Statistical Methods, Communication Skills, Team Building, Innovation

Industry

Software Development

Description

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 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: 5 global recharge days, in addition to standard holidays, and a hybrid, flexible approach to work. Parental leave for all parents, an annual wellness stipend and volunteer days also provide you with time and resources for self care and to care for others. Opportunities throughout the year to learn and celebrate diversity. Access to leading AI tools and foundation models, with the freedom to experiment and find creative ways to be more effective in your role And we're always listening for new ways to support everyone in and out of the office.

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Responsibilities

Lead AI/ML engineering teams to build innovative artificial intelligence solutions. Oversee technical strategy, project management, and team development to drive engineering excellence.