Junior AI​/ML Engineer; Part Time

Location: Remote (UK Only) Type: Part-Time / Flexible (Designed for Students)

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 datacenter 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.

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.

• Format: Part-Time / Flexible. 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.

• 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).

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, Tensor Board, or custom logs) to diagnose issues like overfitting or instability.
• Efficiency:
  
  Experience with LoRA, QLoRA, and acceleration libraries like Unsloth.
• Frameworks: Proficiency in Py Torch , 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
• Dev Ops: Familiarity with Git flow and version control (crucial for tracking experimental changes).

• Currently pursuing or recently completed a Bachelor's or Master's in Computer Science, Machine Learning, Data Engineering, or a related field.

• 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.

Send your resume and a brief introduction to us. We'd love to hear about your experience and what excites you about this role.