hackajob
Manager, Applied AI Engineering, DeepMind

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hackajob is collaborating with Google to connect them with exceptional professionals for this role.
In this role, you will lead the development and deployment of novel applications, leveraging Google’s generative AI models. This role focuses on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google and Google customers. You will be instrumental in translating AI research into real-world products, and demonstrating the capabilities of latest generation models. You will be building and shipping software, and leading engineering teams, ideally with some experience in early-stage environments, where you may have contributed to scaling products from initial concept to production. You will drive product and business impact, and develop strong relationships with research teams.
You will be a passionate machine learning engineering leader with a drive to build innovative products and a desire to work at the forefront of AI.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Minimum Qualifications
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
- 8 years of software development experience, including system design, data structures, and algorithms.
- 7 years of experience leading technical project strategy, ML design, and optimizing industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.
Preferred Qualifications
- 5 years of experience with one or more of the following: media generation, reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- Experience with generative AI research or applications.
- Experience evaluating model performance, analyzing results, and implementing improvements.
- Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc.
- Experience in developing and shipping software products rapidly.
- Contributions to open-source projects.


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Responsibilities
- Lead a team in the design, development, and deployment of scalable generative AI applications and advocating industry best practices.
- Lead the team through the rapid development of new features, iterating based on evaluation results while mentoring members to cultivate a collaborative and high-performing environment.
- Collaborate with researchers and product managers to translate research advancements into tangible product features.
- Oversee the optimization of software performance and ensure the reliability of deployed applications.
- Lead the architecture and development of new products and features from 0 to 1.
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