This Junior Software Engineer role uses Python, Node.js, PostgreSQL, MongoDB, React, and AWS to build backend services, APIs, and frontend applications while supporting commercial machine learning model deployment.

Use Java 17+, Spring Boot, Oracle SQL, document-store databases, Kubernetes, Jenkins, and GitHub Copilot to build high-performance, mission-critical systems that provide end-to-end visibility into payment lifecycles for an investment bank. You will also mentor junior engineers, conduct code reviews, and design scalable back-end solutions.

You will use Azure Databricks, Azure Data Factory, Azure Data Lake Storage Gen2, and Azure SQL/PostgreSQL; develop in Python, Scala, and SQL; and work with Spark, CI/CD, Git, and Docker to build data pipelines. These solutions support HR and employee lifecycle processes within the company.

You'll work with AI agents, LLMs, test frameworks, CI tooling (GitHub Actions), and observability stacks (Datadog, Sentry, Grafana) to automate quality across Lendable's fintech credit products. The role enables cross-functional product teams to maintain high quality through hands-on coding, AI-assisted test authoring, and shift-left practices.

Senior Quality Engineers will build AI Agents, test frameworks, CI tooling and helpers using LLMs and AI tooling, working with GitHub Actions and observability (Datadog, Sentry, Grafana) across an AI-assisted testing function. The team defines Lendable's test pyramid, improves E2E automation, and partners with product and engineering to shift quality left across credit products.

JPMorgan★3.9 (20k)· London

In this role, you will work with Go, Python, Java, Kubernetes, Terraform, and public cloud providers like AWS, Azure, or GCP. You will help build a new Identity and Access Management (IAM) team within JPMorgan's Public Cloud Engineering organization. As a Lead, you will guide architecture, mentor engineers, and drive platform services for cloud infrastructure.

You will use PyTorch or JAX, Python, and DFT packages like VASP or Quantum Espresso to develop machine learning interatomic potentials (MLIPs) that accelerate discovery of advanced functional materials by integrating ML with first-principles physics and automated experimental data.

Fine-tune and distill small language models (Llama, Mistral) using quantization (GGUF, AWQ) and inference engines (vLLM, TensorRT-LLM). Presential Limited builds the privacy layer for enterprise AI, enabling tier-1 banks to securely use sensitive data with external models. As founding AI engineer, you’ll own the model layer and ship production solutions.

You will work with Python, Django, data pipelines, algorithms, and machine learning models to build an AI-powered engine that automates electronic circuit board design from requirements to manufacture-ready outputs.

You'll work with Java (8, 17, 21), React/TypeScript (or Angular/Vue), and REST APIs to sustain current environments and deliver new capabilities across full-stack development. You will also mentor less experienced engineers through code reviews and technical guidance.

Full Stack Engineer role at G LNK (YC W25), an AI healthcare commercial intelligence platform. You will build AI-driven matchmaking systems using React, TypeScript, and Python, managing a database of 9M+ clinician profiles. Work directly with founders on scalable data pipelines and healthcare commercialization workflows.

You will lead a UK data team across Data Science, ML Engineering, Analytics Engineering, and Data Engineering, working with GCP, Databricks, and Redshift. The team supports Juniper, a weight-management program combining GLP-1 medication with clinical and behavioural care. You will manage 4-5 people and oversee migrations from Databricks and Redshift to GCP.

Imperial College London★4.2 (238)· London

Wellcome Trust-funded postdoc at Imperial College London developing deep neural networks for volumetric voltage imaging of mammalian brains, using optimisation, signal processing, lightfield imaging, and model-based deep learning/training.

You will work with Python, deep learning image models, machine learning and large image and tabular datasets. The KYC Global and Onboarding Team implements ML models and systems supporting Document verification for customer onboarding. You will own the full lifecycle of these models and mentor junior team members.

Glasswall★0.0· London

You'll work with C++, C#, Python, Assembly, and tools like static/dynamic analyzers, debuggers, disassemblers, detonation chambers, and agentic AI. As a Senior Security Researcher on a small team, you'll investigate threats to Glasswall's Content Disarm and Reconstruction technology through vulnerability research, penetration testing, and malware analysis.

In this role, you will conduct security assessments, develop policies, and identify vulnerabilities for AI systems and applications. You will provide compliance expertise and collaborate with cross-functional teams.

Flutter Entertainment★3.9 (96)· London

You will use generative AI coding tools (ChatGPT, Claude, Midjourney) and Python for prototype development, while conducting technology horizon scanning and evaluating new AI platforms. The Frontier R&D team identifies and prototypes emerging technologies over a 12-36 month horizon for Flutter’s betting and gaming brands. This is a graduate-level individual contributor role.

You will design and train custom deep-learning models and fine-tune large language models using Python, TensorFlow, or PyTorch. The work involves leading research for Kangal AI, a venture building frontier AI for governments and security clients. You will own the entire model stack and scale a research team as a founding hire.

Brown-Forman★3.6 (194)· London

The Marketing Director will drive AI transformation within the marketing organization, leveraging advanced AI, the existing MarTech stack, and tools like generative AI and automated dashboards. This role defines and implements the company's marketing AI strategy across the entire marketing ecosystem, optimizing processes such as brand planning, campaign development, and media planning. The position leads and collaborates within a cross-functional AI Center of Excellence (COE).

Methods Business and Digital Technology★4.5 (2)· London

A Data Architect role at Methods Analytics, working mainly remotely with travel to client sites, designing end-to-end data solutions, migrating legacy systems, and setting technical standards. The position requires deep knowledge of the Azure data stack (Azure SQL, CosmosDB, ADF, Purview, Synapse, Databricks), data modelling, Kafka, and Delta Lake, and may require UK security clearance.

Senior Customer Success Manager role owning client relationships for a secure AI knowledge platform that helps lawyers draft, negotiate, and review contracts. You will drive adoption of AI-powered workflows (machine learning, generative AI, language analytics) on a small, fast-moving team.

Work with Python, Nextflow/Snakemake, SMILES/SMARTS/SAFE, molecular fingerprints, LC-MS/MS data (mzML, MSConvert), and spectral databases (GNPS, MassBank, MoNA) to build schemas, QC pipelines, and datasets. You'll join Novogaia's small team creating foundation models that predict molecular structure from mass spectrometry data for natural-product drug discovery.

You'll work with Python, reproducible/containerized pipelines, cheminformatics representations (SMILES, InChIKey, fingerprints), and computational mass spectrometry. Novogaia is an applied AI drug discovery company building foundation models for molecular structure prediction from mass spectrometry data, and you'll own the design of internal benchmarks and evaluation pipelines for these models.

Imperial College London★4.2 (238)· London

You will develop novel AI methods advancing fairness, interpretability, robustness, safety, privacy and human-AI interaction. You join the Dyson School of Design Engineering to conduct high-impact research and deliver teaching on fair and responsible AI, contributing to Imperial’s Science for Humanity strategy.

Glasswall★0.0· London

Work with Python, PyTorch, Scikit-learn, Hugging Face, Git, CI/CD, and MLOps tools to build, tune, and optimize machine learning, deep learning, and generative AI models. You will design LLM-powered systems (RAG pipelines, fine-tuning, agentic AI architectures) and production-grade pipelines within the Applied AI team at Glasswall. This hands-on role includes providing technical guidance and mentoring to mid-level and junior engineers.