In the U.S., the standard base pay range for this role is $20.00 - $50.00 Hourly. These values reflect the expected base pay range of new hires across all U.S. locations. Ultimately, your specific range and offer will be based on several factors, including relevant experience, performance, and work location. Your Talent Professional can share this role’s range details for your local geography during the hiring process. In addition to a competitive base pay, employees in this role may be eligible for incentive compensation. Incentive compensation is not guaranteed. While we strive to provide competitive offers to successful candidates, new hire compensation is negotiable.
Responsibilities
Apply quantitative methods to game data
Use SQL and statistical software to analyze player behavior
Provide insights to game developers and product managers
Graduate program in Data & AI and Digital Engineering at bp, involving design, development, and support of digital solutions across cloud platforms and AI technologies.
Compensation
Competitive base salary
One-time sign-on bonus
Annual cash bonus opportunity
Responsibilities
Design, build, automate, and maintain secure, reliable, and scalable cloud-based solutions using technologies across AWS and Azure.
Develop software applications, services, and APIs using modern programming languages.
Participate in web and mobile application development using modern frameworks and development practices.
Support the implementation of engineering best practices, including agile delivery, automation, testing, observability, security, and reliability.
Collaborate with business subject matter experts, product teams, data engineers, data scientists, analysts, and platform engineers to solve complex business challenges.
Build and deploy production‑grade machine learning software, tools, and infrastructure, collaborating with engineers, data scientists, and commercial leads to solve client challenges.
Responsibilities
Building and deploying production‑grade ML software, tools, and infrastructure
Creating reusable, scalable solutions that accelerate the delivery of ML systems
Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges
Leading technical scoping and architectural decisions to ensure project feasibility and impact
Defining and implementing Faculty’s standards for deploying machine learning at scale
A hands-on leadership role for practitioners operating at the cutting edge of LLMs and agentic systems, designing and delivering production-grade AI solutions that are safe, reliable, and scalable.
Locations (2)
Melbourne, Victoria, Australia
Sydney, New South Wales, Australia
Responsibilities
Architect and design agent-based AI systems powering NAB’s core AI products
Define prompting strategies, orchestration patterns, and context management approaches
Lead technical design decisions ensuring AI systems are safe, accurate, and reliable
Translate AI architecture into production systems in partnership with engineering teams
Drive delivery of advanced AI solutions through a high-performing data science team
Quantitative researcher intern for the summer of 2027, developing research skills and working on new quant trading models.
Compensation
The annual base salary is $240,000-$300,000 (USD) which will be prorated based on internship start and end date. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
Responsibilities
Develop research skills through training
Work closely with full-time researchers on new quant trading models
Receive rigorous and comprehensive training
Potential full-time offer after internship
Requirements
PhD candidate in math/physics/statistics/EE/CS or other quantitative fields
Build and backtest quantitative trading models, analyze high-frequency market data, and collaborate with traders and researchers to refine systematic strategies.
Responsibilities
Build, backtest, and refine quantitative trading models using historical market and orderbook data
Analyze large datasets to identify patterns, inefficiencies, and alpha signals for systematic strategy development
Monitor real-time trading positions and market conditions, assisting traders with risk management and parameter adjustments
Collaborate with quantitative researchers and software developers to implement strategy prototypes into the firm's low-latency execution infrastructure
Oversee and improve daily trading processes as needed
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