At a Glance
- Tasks: Lead the development of a cutting-edge platform for large language model training and optimisation.
- Company: Join Scale, a leader in AI solutions and ML lifecycle support.
- Benefits: Enjoy comprehensive health coverage, flexible schedules, and personal growth opportunities.
- Other info: Be part of a vibrant community with fun events and supportive work-life balance.
- Why this job: Make a real impact in AI by optimising ML systems and collaborating with top researchers.
- Qualifications: Strong software engineering skills and experience with large-scale distributed ML systems.
The predicted salary is between 63000 - 77000 £ per year.
- Scale’s LLM post-training platform team builds our internal distributed framework for large language model training.
The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs.
It also serves as the underlying training framework for the data quality evaluation pipeline
- Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle.
You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works.
You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation
- Build, profile and optimize our training and inference framework
- Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation
- Research and integrate state-of-the-art technologies to optimize our ML system
Benefits
- Health & Wellbeing: Our holistic approach to supporting Scaliens includes comprehensive health coverage, dental and vision insurance, mental healthcare services, and more.
PTO policies and accommodating schedules ensure you’ll get time off when you need it to relax and recharge.
Note that our offerings may vary by region as we strive to respond to the unique needs of Scaliens around the globe.
- Personal & Career
Growth: Continuously learn and grow through annual learning & development stipend, attending leadership breakfasts, manager training, speaker series, and joining an ERG.
- Building
- Scale
Community: We welcome guests to our offices, and you can expect to see Scalien families and friends around.
Join local happy hours, and accept invites to game nights, book clubs, and many other employee-led community events.
- Parental Support: Balancing work and family is essential, and Scale understands the importance of having adequate leave policies in place to promote a healthy home and work life.
Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc Passionate about system optimization Experience with developing large-scale distributed ML systems Strong written and verbal communication skills to operate in a cross functional team environment Experience with multi-node LLM training and inference Demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc
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Tech Lead Manager (MLRE, ML Systems) employer: Scale AI
At Scale, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration among software and ML engineers. Our London-based team is dedicated to pushing the boundaries of AI technology while providing ample opportunities for professional growth and development, ensuring that every employee can contribute meaningfully to impactful projects. With a commitment to inclusivity and equal opportunity, we create an environment where everyone can thrive and bring their whole selves to work.
StudySmarter Expert Advice🤫
We think this is how you could land Tech Lead Manager (MLRE, ML Systems)
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Scale AI or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Scale AI.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Scale AI.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Scale AI that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Tech Lead Manager (MLRE, ML Systems)
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 Scale AI.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Scale AI 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 Scale AI
✨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 Scale AI 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.
