4 of 4 Permanent Distributed Computing Jobs in Berkshire

curation workflows essential for efficient LLM training and inference. Model Development & Evaluation Lead the development and evaluation of LLMs, leveraging expertise in transformers , self-supervised learning , and large-scale distributed training . Optimize training pipelines for enhanced performance, compute efficiency, and responsible AI alignment. LLM Deployment & Scalability Manage the end-to-end deployment of LLMs and foundation models in … AI pipelines, with demonstrated involvement in LLM development, fine-tuning, or deployment. Technical Skills: Proficiency in Python and frameworks such as PyTorch , TensorFlow , or JAX . Strong familiarity with distributed computing and data engineering tools (e.g., SLURM , Apache Spark , Airflow ). Hands-on experience with LLM training, fine-tuning, and deployment (e.g., Hugging Face , LLamafactory , NVIDIA NeMo ). More ❯

orchestration (Kubernetes). Excellent problem-solving skills and ability to work independently in a fast-paced environment. Desirable: Experience with NLP, computer vision, or time-series forecasting. Familiarity with distributed computing frameworks (Spark, Ray). Experience with MLOps and model governance practices. Previous contract experience in a similar ML engineering role. Contract Details Duration: 6–12 months (extension More ❯

on experience with open-source data frameworks like Apache Spark, Apache Kafka, and Apache Airflow. Strong proficiency in SQL, including advanced query development and performance tuning. Good understanding of distributed computing principles and big data ecosystems. Familiar with version control tools (Git) and CI/CD automation pipelines. Experience working with relational databases such as PostgreSQL, MySQL, or More ❯

financial algorithms. Translate mathematical and operations research models into enterprise-grade solutions. Collaborate with developers and product teams to frame problems as solvable models. Contribute to the architecture of distributed, high-throughput systems. Conduct research to enhance optimisation frameworks and algorithm performance. Required Experience Strong foundation in mathematics, optimisation, or operations research. Demonstrated Python engineering skills (OOP, algorithms, data … in a quantitative discipline. Experience with optimisation frameworks (Gurobi, OR-Tools). Knowledge of financial markets, derivatives, or clearing/margin optimisation. Hands-on experience with AWS, PostgreSQL, or distributed computing. Why Join? Solve complex mathematical challenges with global financial impact. Work in a collaborative, research-driven team where curiosity and creativity are valued. Benefit from a competitive salary More ❯