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Develop real-time, resource-efficient machine learning models for computer vision and user input systems, primarily for Apple's Vision Pro, collaborating across hardware, software, and design teams.
Senior ML researcher on Apple's SIML Content Understanding team, architecting and deploying production-scale multimodal ML for Apple Intelligence features like Image Playground, Genmoji, and Semantic Search. Day-to-day spans LLM training/adaptation, NLP+Vision modeling, embeddings, RAG, and agentic prototyping, mainly in PyTorch and Python.
Lead the ML platform for payment risk at Breeze (Singapore): build production-grade feature pipelines, model training, deployment, and monitoring on Databricks for real-time fraud/risk decisions. Reports to the CTO as the senior ML technical voice on the risk team.
Leads and scales EarnIn's machine learning organization, owning the ML roadmap and managing multiple teams that move models from research to production for its earned wage access fintech product. Requires deep ML engineering depth (Python, SQL), 12+ years experience, and hands-on leadership.
EarnIn is hiring a Head of Machine Learning for its Bengaluru site to build, manage, and mentor an ML engineering team and own delivery of production ML systems (deep learning, reinforcement learning) powering risk, fraud, growth, and personalization. It's a hands-on leadership role requiring strong Python/SQL and production ML experience, hybrid with 2+ days in office.
TomTom is hiring a Staff ML Engineer in Berlin to lead algorithmic direction for its ADAS Online in-vehicle 3D spatial awareness stack — designing vision transformer, diffusion, and Gaussian Splatting models and multi-modal sensor fusion (camera, LiDAR, RADAR) combined with real-time HD map data, hands-on in PyTorch.
A quantitative developer joins a systematic equities trading pod at a global hedge fund, building and deploying classical and deep learning models on high-frequency market data and scaling distributed research-to-production workflows. Core stack: Python, distributed computing, Linux, with C++ an advantage.
A senior engineering leadership role in JPMorganChase's AI/ML & Data Platforms group in London: leading a technical area and cross-functional teams, driving AI-assisted engineering practices across the SDLC, and building cloud-based (AWS) applications. Requires 10+ years of software engineering experience.
AI/ML Engineer at Mphasis building AI-driven quality assurance solutions: developing Generative AI and RAG applications with Python and LLMs (GPT, Claude), deploying via Docker/Kubernetes, and applying ML algorithms and automated testing, with optional React front-end work.
Lead iProov's Biometric Systems function, taking biometric/computer vision systems from research and prototyping through to production while owning metrics like precision, recall, bias, speed and robustness. Hands-on with Python, OpenCV, PyTorch/TensorFlow and Docker, plus cross-functional leadership, roadmapping and mentoring, in a hybrid London role.
Software engineer on Apple's HRES AI/ML team in San Diego, building and maintaining AI/ML and Generative AI services (infrastructure, evaluation pipelines, integrations) that support Apple's hardware product development. Day-to-day work centers on Python, ML/LLM applications, and cloud deployment like Kubernetes.
Lead AI-powered product strategy for manufacturing software, deploying ML pipelines for factory sensing, document processing, and agentic workflows to automate operations and improve efficiency.
Contract AI/ML engineer (up to £700/day) supporting delivery of in-flight programmes for UK Government and Defence customers. The team builds applications for warfighters that interact with next-generation hardware; an active SC security clearance is required.
Principal Research Engineer at J.P. Morgan designing autonomous AI agents that can reason, plan, act, and learn to solve critical problems for a leading financial institution. Part of the Applied AI/ML team in Commercial & Investment Banking, focused on defining the future of banking through Agentic AI.
Design and build scalable ML systems and pipelines for a massive media audience, deploying models and leading architecture decisions in an AI-first rebuild.
First ML engineer at Metriport, a healthcare data-interoperability startup that turns raw medical records into clean, usable data. You own ML end-to-end: framing problems, building predictive models on large clinical datasets, parsing unstructured records, and running ML infrastructure, using Python, TypeScript, Node.js, SQL, and AWS.
A 10-week summer internship at a quantitative investment firm where you design and evaluate machine learning systems for systematic investing — building scalable data pipelines and applying modern ML to large market datasets. Core stack includes Python, Rust, or C/C++ with PyTorch, TensorFlow, or JAX.
Research and build next-gen post-training algorithms for enterprise AI agents, optimizing LLM training/inference frameworks and collaborating with ML teams to deploy state-of-the-art models.
Build and train next-gen AI agents using reinforcement learning to power enterprise GenAI systems across industries like cybersecurity and healthcare.
Research and train cutting-edge AI agents using post-training methods like RLHF/RLVR to deploy state-of-the-art models for enterprise clients.
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