Senior Silicon Power Engineer in Singapore

About the Team OpenAI’s Legal team plays a crucial role in advancing our mission by tackling innovative and fundamental legal issues in AI. The team includes professionals from diverse legal fields—technology, AI, infrastructure, privacy, IP, corporate, employment, tax, regulatory, and litigation—who collaborate closely with colleagues across the company. If you are passionate about being a technology lawyer working on cutting-edge challenges, you’ll thrive here. About the Role We are seeking an experienced commercial attorney to join our commercial legal team. You will help lead commercial legal support for OpenAI’s rapidly growing business across the APAC region, working with teams such as GTM, Partnerships, Procurement, Finance, Product, Government Affairs, Infrastructure, Security, and Engineering. This is a great opportunity to join OpenAI’s commercial legal team, negotiating high-impact agreements and helping to build scalable frameworks for responsible growth. This role is based in Singapore. We use a hybrid work model of 3 days in the office per wee In this role, you will: Serve as the legal partner for OpenAI’s business transactions, partnerships, and operations. Provide practical, business-oriented legal advice to teams including Sales, Partnerships, Finance, Procurement, and Operations. Identify and address commercial, product, regulatory, privacy, and other legal issues arising from commercial transactions. Develop strategies and processes for handling legal issues in creative and scalable ways that support growth. Monitor and advise on regulatory and compliance matters affecting AI and technology. Collaborate with colleagues across OpenAI’s global legal team to ensure alignment across jurisdictions. You might thrive in this role if you: Have 10+ years of experience across in-house and technology-focused law firm roles. Bring a strong background in drafting and negotiating complex commercial contracts. Are comfortable operating in an entrepreneurial envi

Senior Engineer / Engineer - HIG HBM PSE — Fab 10A, Singapore. Apply via Workday.

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. What you'll do Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the m

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