Lead and develop control algorithm models for HVAC and transportation refrigeration systems, driving innovation and optimization across Trane Technologies.
Compensation
We offer competitive compensation and comprehensive benefits and programs.
Responsibilities
Develop and implement complex control algorithm models
Demonstrate software engineering concepts for Model Based Development
Participate in technology roadmap discussions
Work with internal and external partners on new technology projects
Lead evaluation of new technologies for commercial value
Requirements
Master’s or Ph.D. in Electrical, Electronics, Computer Science, or related fields
Nice to have: coaching & mentoring technical know-how
Lead the architecture, development, deployment, and ongoing evolution of AI initiatives across a global consumer SuperApp. This is a hands-on technical leadership role focused on open-source foundation models, self-hosted infrastructure, and production AI systems.
Compensation
Compensation is accrued and payable following funding, after which it transitions to a regular salary, equity ownership, and OleX token participation in the overall OleOle group of companies.
Responsibilities
Establish overall AI architecture
Lead initiatives including AskOle
Lead real-time multilingual translation
Develop AI-powered sports history and content generation systems
Develop the sports data engine
Requirements
Nice to have: building consumer AI platforms at scale
Nice to have: multilingual and multimodal AI engineering
Design and deploy large-scale HPC and GPU infrastructure and architect scalable AI solutions on OCI, serving as a technical advisor for enterprise customers.
Responsibilities
Design and deploy large-scale HPC and GPU infrastructure on Oracle Cloud Infrastructure (OCI)
Architect scalable AI and accelerated computing solutions using Infrastructure as Code (Terraform, Ansible) and Kubernetes.
Develop automated deployment frameworks for cluster provisioning, software installation, and lifecycle management.
Partner with enterprise customers to design solutions that meet AI, machine learning, and high-performance computing requirements.
Design and support Large Language Model (LLM), Agentic AI, and AI platform deployments from proof-of-concept through production.
Requirements
Bachelor's degree in Computer Science, Engineering, Information Technology, or a related technical discipline
Nice to have: Experience with Oracle Cloud Infrastructure (OCI)
Nice to have: Experience with large GPU clusters based on Kubernetes and/or Slurm
Nice to have: Experience supporting AI startups, research organisations, or enterprise AI programmes
Nice to have: Oracle Cloud certifications or equivalent cloud certifications (AWS, Azure, or Google Cloud)
Lead the engineering organization responsible for defining, building, and delivering complex data and AI infrastructure software.
Compensation
The target salary range for this position is 266,050 - 396,000 USD.
Responsibilities
Own end-to-end engineering delivery, from design through implementation, testing, and production reliability.
Provide deep technical leadership, design reviews, and guidance on performance, scalability, security, and system tradeoffs, including accelerated computing where relevant; partner with senior architects to turn technical direction into robust products.
Translate product priorities and customer commitments into realistic engineering plans with clear owners, milestones, dependencies, quality gates, and measurable outcomes.
Build a disciplined execution culture with high engineering velocity, transparent progress, timely decisions, and accountability for commitments.
Recruit, coach, and retain exceptional engineers and engineering leaders; define team structures, clarify decision rights, develop successors, and raise the technical bar.
Data scientist with strong background in data mining, machine learning, recommendation systems, and statistics.
Responsibilities
Work with customers to deliver ML/DL projects from beginning to end
Select features and build classifiers using ML techniques
Build and optimize predictive models
Deploy completed models to deliver business impact
Create text mining pipelines to clean and process large unstructured datasets
Requirements
Master’s or PhD Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience
Lead the engineering organization responsible for defining, building, and delivering complex data and AI infrastructure software.
Compensation
The target salary range for this position is 266,050 - 396,000 USD.
Responsibilities
Own end-to-end engineering delivery, from design through implementation, testing, and production reliability.
Provide deep technical leadership, design reviews, and guidance on performance, scalability, security, and system tradeoffs.
Translate product priorities and customer commitments into realistic engineering plans with clear owners, milestones, dependencies, quality gates, and measurable outcomes.
Build a disciplined execution culture with high engineering velocity, transparent progress, timely decisions, and accountability for commitments.
Recruit, coach, and retain exceptional engineers and engineering leaders; define team structures, clarify decision rights, develop successors, and raise the technical bar.
Design and build the core research and trading platform, including data pipelines, backtesting and simulation frameworks, portfolio and execution tooling, and research APIs.
Compensation
The annual base salary range for this position is $175,000 to $250,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus.
Responsibilities
Design and build the core research and trading platform, including data pipelines, backtesting and simulation frameworks, portfolio and execution tooling, and research APIs
Work closely with quantitative researchers to implement studies, test hypotheses, and translate promising ideas into robust production systems
Develop and productionize statistical and machine learning models, owning the workflow from feature generation and training through backtesting, deployment, and live monitoring
Build reliable data infrastructure for large historical and real-time datasets, with an emphasis on point-in-time correctness, reproducibility, performance, and ease of use
Improve the performance and scalability of computationally intensive research and production workloads
Requirements
Bachelor's degree in computer science, computer engineering, or another technical field
Master's degree in computer science, computer engineering, or another technical field
PhD degree in computer science, computer engineering, or another technical field
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