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For Companies At a Glance
- Tasks: Run experiments, analyse metrics, and optimise AI models with senior researchers.
- Company: Always Further, a cutting-edge startup in AI/ML development.
- Benefits: Flexible part-time hours, mentorship from experts, and impactful projects.
- Why this job: Make a real impact on AI technology while fitting around your studies.
- Qualifications: Experience in model building, Python skills, and familiarity with AI frameworks.
- Other info: Join a diverse team and contribute to open-source AI projects.
The predicted salary is between 13 - 16 £ per hour.
Location: Remote (UK Only)
Type: Part-Time / Flexible (Designed for Students)
About Always Further
Always Further is an exciting startup developing cutting-edge approaches to model training and evaluation. We build open-source tools and infrastructure to make it easy for developers and organizations to create, fine-tune, and evaluate specialized AI models that are efficient, transparent, and privacy-respecting. We believe AI shouldn’t require a datacentre and its own energy grid to be useful. Backed by leading investors in AI and technology, our team has built open-source projects used by thousands of developers worldwide.
The Role & Context
We are seeking a Junior AI/ML Engineer to work directly alongside the founders and our senior researchers to build models and validate new training techniques. This role is specifically designed to fit around university coursework and exams.
The Focus: This is an experimental role. You won’t just be maintaining pipelines; you will be running training jobs, staring at loss curves, analyzing benchmarks, and figuring out how to squeeze more performance out of smaller models.
Mentorship: You will pair closely with senior researchers and the founders, turning their hypotheses into code and results.
What You’ll Do
- Run Rigorous Experiments: Execute fine-tuning runs (SFT, GRPO/PPO) alongside senior researchers to test new data mixtures, hyperparameters, and optimization techniques.
- Measure & Analyze: Quantify model improvements using evaluation benchmarks. You will track metrics (loss curves, perplexity, task accuracy) to determine if an experiment was a success or failure.
- Optimize Performance: Actively hunt for efficiency gains - whether that’s reducing memory usage with Unsloth/QLoRA, optimizing data loading, or speeding up inference.
- Build Research Infrastructure: Develop the tooling needed to make experiments reproducible and faster to run (e.g., automated eval harnesses).
Required Qualifications
We value project experience and coursework as much as professional experience.
- Model Building: Hands-on experience fine-tuning LLMs. You understand the "loop" of training, evaluating, and adjusting hyperparameters.
- Measurement: Ability to analyze training metrics (e.g., using Weights & Biases, TensorBoard, or custom logs) to diagnose issues like overfitting or instability.
- Efficiency: Experience with LoRA, QLoRA, and acceleration libraries like Unsloth.
- Frameworks: Proficiency in PyTorch, Hugging Face Transformers, and TRL.
- Languages: Strong Python skills are required; interest in Go is a bonus.
- Cloud & Containers: Exposure to running jobs on cloud GPUs (AWS or GCP) and using Docker and Terraform.
- DevOps: Familiarity with Git flow and version control (crucial for tracking experimental changes).
Education
Currently pursuing or recently completed a Bachelor’s or Master’s in Computer Science, Machine Learning, Data Engineering, or a related field.
Why Join Us
- Research to Production: Don’t just read papers - implement them. See how theoretical research gets optimized for real-world usage.
- High Impact: Your experiments will directly influence the architecture and training methods of our core products.
- Open Source: A chance to contribute to open-source AI/ML projects visible to the community.
- Ownership: An early-stage opportunity to own major technical initiatives.
Always Further is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Ready to apply? Send your resume and a brief introduction to us. We’d love to hear about your experience and what excites you about this role.
Junior AI/ML Engineer (Part Time) employer: Always Further, Inc.
Contact Detail:
Always Further, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Junior AI/ML Engineer (Part Time)
✨Tip Number 1
Network like a pro! Reach out to people in the AI/ML community on LinkedIn or Twitter. Join relevant groups and participate in discussions. You never know who might have a lead on a job or can refer you directly!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to model training and evaluation. Share it on GitHub and make sure to link it in your applications. This gives us a glimpse of what you can do!
✨Tip Number 3
Prepare for interviews by practising common technical questions and coding challenges. Use platforms like LeetCode or HackerRank to sharpen your skills. We want to see how you think and solve problems, so be ready to showcase your thought process!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Tailor your cover letter to highlight your passion for AI/ML and how you align with our mission at Always Further. Let’s make your application stand out!
We think you need these skills to ace Junior AI/ML Engineer (Part Time)
Some tips for your application 🫡
Show Your Passion for AI/ML: When you're writing your application, let us see your enthusiasm for AI and machine learning! Share any projects or coursework that got you excited about the field. We want to know what drives you!
Tailor Your Resume: Make sure your resume highlights relevant experience, especially with model building and fine-tuning. Use keywords from the job description to show us you’re a great fit. It’s all about making it easy for us to see how you match the role!
Be Clear and Concise: Keep your introduction brief but impactful. We appreciate clarity, so get straight to the point about your skills and what excites you about this opportunity. Remember, we’re looking for potential and passion!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Always Further, Inc.
✨Know Your Models
Make sure you brush up on your knowledge of model training and fine-tuning techniques. Be ready to discuss your hands-on experience with LLMs and how you've tackled challenges like overfitting or instability in the past.
✨Show Your Analytical Skills
Prepare to talk about how you measure and analyse training metrics. Familiarise yourself with tools like Weights & Biases or TensorBoard, and be ready to share specific examples of how you've used these to diagnose issues in your projects.
✨Demonstrate Your Efficiency Mindset
Always Further values efficiency, so think about ways you've optimised performance in previous projects. Whether it's reducing memory usage or speeding up inference, have concrete examples ready to showcase your problem-solving skills.
✨Be Ready for Technical Questions
Expect some technical questions related to frameworks like PyTorch and Hugging Face Transformers. Brush up on your Python skills and be prepared to discuss your familiarity with cloud services and version control systems like Git.
