At a Glance
- Tasks: Lead groundbreaking research in AI and develop impactful systems for global public sector applications.
- Company: Join Scale, a leader in AI innovation with a mission to make a difference.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Collaborative environment focused on innovation and career advancement.
- Why this job: Shape the future of AI while making a real-world impact on millions of lives.
- Qualifications: Advanced degree in Computer Science or related field, with strong research and coding skills.
The predicted salary is between 60000 - 80000 € per year.
About Scale
Scale's mission is to develop reliable AI systems for the world's most important decisions. Our core work consists of:
- Creating custom AI applications that will impact millions of citizens
- Generating high-quality training data for national LLMs
- Upskilling and advisory services to spread the impact of AI
Scale is hiring ML Research Engineers to bridge the gap between frontier research and real-world impact. While we solve critical challenges for global governments, your role will extend beyond implementation. You will lead the charge in research into Agent design, Deep Research and AI Safety/reliability, developing novel methodologies that not only power public sector applications but set new standards across the entire Scale organisation.
Your Mission
- Frontier Research & Publication: Leading research into LLM/agent capabilities, reasoning, and safety, with the goal of publishing at top-tier venues (NeurIPS, ICML, ICLR).
- Cross‐Org Impact: Developing generalized techniques in Agent design, AI Safety, and Deep Research agents that scale across our commercial and government platforms.
- Mission‐Critical Applications: Engineering high‐stakes AI systems that impact millions of citizens globally.
You Will
- Pioneer Novel Architectures: Design and train state‐of‐the‐art models and agents, moving beyond 'off‐the‐shelf' solutions to create custom architectures for complex public sector reasoning tasks.
- Lead AI Safety Initiatives: Research and implement robust safety frameworks, including red teaming, alignment (RLHF/DPO), and bias mitigation strategies essential for sovereign AI.
- Drive Deep Research Capabilities: Develop agents capable of long‐horizon reasoning and autonomous information synthesis to solve complex problems for national security and public policy.
- Publish and Contribute: Represent Scale in the broader research community by publishing high‐impact papers and contributing to open‐source breakthroughs.
- Consult as a Subject Matter Expert: Act as a technical authority for public sector leaders, advising on the theoretical limits and safety requirements of emerging AI.
- Build Evaluation Frontiers: Create new benchmarks and evaluation protocols that define what success looks like for high‐stakes, non‐commercial AI applications.
Ideally, You'd Have
- Advanced Degree: PhD or Master's in Computer Science, Mathematics, or a related field with a focus on Deep Learning.
- Research Track Record: A portfolio of first‐author publications at major conferences (NeurIPS, ICML, CVPR, EMNLP, etc.).
- Engineering Rigor: Strong proficiency in Python, deep learning frameworks (PyTorch/JAX), with the ability to write production‐ready code that scales.
- Safety Expertise: Experience in alignment, robustness, or interpretability research.
Nice to Haves
- Experience with large‐scale distributed training on massive clusters.
- Experience in building agentic systems that are reliable.
- Experience in Sovereign AI or working with highly regulated data environments.
- A zero‐to‐one mindset: Comfortable navigating ambiguity and defining research directions from scratch.
Legal and Compliance Statements
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com.
We comply with the United States Department of Labor's Pay Transparency provision.
Machine Learning Engineer (Global Public Sector) in London employer: Scale AI
At Scale, we are dedicated to fostering a collaborative and innovative work environment where Machine Learning Engineers can thrive. Our commitment to employee growth is evident through opportunities for pioneering research, publishing in top-tier venues, and contributing to impactful AI systems that serve the global public sector. With a focus on inclusivity and support for diverse backgrounds, we ensure that every team member can bring their authentic selves to work while making a meaningful difference in the world.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer (Global Public Sector) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI and machine learning. This is your chance to demonstrate your expertise and make a lasting impression.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common ML interview questions and be ready to discuss your research and projects in detail.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our mission at Scale.
We think you need these skills to ace Machine Learning Engineer (Global Public Sector) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Machine Learning Engineer role. Highlight your research track record and any relevant projects that showcase your expertise in AI safety and deep learning.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a perfect fit for Scale. Don’t forget to mention any specific projects or publications that relate to the job description.
Showcase Your Research:If you've got publications, make them front and centre! Mention your first-author papers and any contributions to top-tier conferences. This will demonstrate your commitment to advancing the field and your ability to contribute to Scale's mission.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Scale AI
✨Know Your Research
Make sure you’re well-versed in your own research and publications. Be ready to discuss your contributions in detail, especially those that align with Scale's mission of developing reliable AI systems. This shows not only your expertise but also your passion for the field.
✨Understand the Role
Familiarise yourself with the specific responsibilities of a Machine Learning Engineer at Scale. Think about how your skills in deep learning frameworks and AI safety can contribute to their mission-critical applications. Tailor your answers to demonstrate how you can bridge the gap between research and real-world impact.
✨Prepare for Technical Questions
Expect technical questions that assess your proficiency in Python and deep learning frameworks like PyTorch or JAX. Brush up on your coding skills and be prepared to solve problems on the spot. Practising common algorithms and data structures can give you an edge.
✨Showcase Your Problem-Solving Skills
Be ready to discuss complex problems you've solved in the past, particularly those related to AI safety and agent design. Use the STAR method (Situation, Task, Action, Result) to structure your responses, highlighting your ability to navigate ambiguity and define research directions from scratch.