At a Glance
- Tasks: Scale data optimisation techniques to enhance ML model performance and quality.
- Company: Join Google Cloud, a leader in next-gen technology and innovation.
- Benefits: Competitive salary, health benefits, remote work options, and career development opportunities.
- Other info: Collaborate with top teams and shape the future of hyperscale computing.
- Why this job: Make a real impact on AI and data optimisation in a fast-paced environment.
- Qualifications: 5 years of experience in Python, data structures, and ML infrastructure.
hackajob is collaborating with Google to connect them with exceptional professionals for this role.
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. In this role, you will be a part a team operating in the Gemini Era, where AI is profoundly data-centric, the “quality” data used for training, fine-tuning, or Retrieval-Augmented Generation (RAG) has greater impact one end product performance than almost anything else. Your mission is to improve the time to model quality for users, achieved by bringing data optimization techniques to a broad audience through integrated tools and platforms. In this role, the tooling automatically and efficiently applies data optimization techniques, and runs structured ablations to demonstrate to users which ones work best for their use case, and deliver insights on how to improve further. You will collaborate with key product teams across Google. The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world. We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Minimum Qualifications
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with data structures and algorithms in Python.
- 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
- Experience with data analysis, data optimizations and data evaluations.
Preferred Qualifications
- Master's degree or PhD in Computer Science or a related technical field.
- 5 years of experience working in a complex, matrixed organization.
- Experience in Gen AI.
- Experience in data optimization and data platforms.
- Experience with research to production.
Responsibilities
- Scale data optimization techniques (including those from Google Research) to enhance the performance and quality of ML models, while heavily shaping the technical direction of data platforms for model tuning across multiple product areas.
- Establish technical relationships with multiple product areas to define strategy and drive technical directions.
- Leverage existing assets and techniques from Google Research to accelerate model tuning velocity for teams like Bard and other product areas fine-tuning Gemini models.
- Collaborate with Research teams and ML practitioners to identify, build and iterate on engineering tools, processing pipelines, data optimization techniques, integration with existing workflows, user interfaces and supporting users adoption.
- Apply research and enage directly with users to advance Google’s goal of making AI helpful for everyone.
AI/ML Senior Software Engineer, Data Optimization and Platform employer: hackajob
At hackajob, we pride ourselves on being an exceptional employer that fosters a culture of innovation and inclusivity. Our diverse team thrives in a high-performance environment where your contributions directly impact our multi-asset platform's success. With ample opportunities for professional growth and a commitment to employee development, joining us means being part of a forward-thinking company that values your expertise and ambition.
StudySmarter Expert Advice🤫
We think this is how you could land AI/ML Senior Software Engineer, Data Optimization and Platform
✨Join Local Tech Meetups
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✨Contribute to Open Source Projects
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We think you need these skills to ace AI/ML Senior Software Engineer, Data Optimization and Platform
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at hackajob.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at hackajob and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at hackajob
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If hackajob uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
