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Build and scale AI systems for ASOS Studios that process product imagery and video for millions of shoppers, applying deep learning embeddings, computer vision, and generative AI/LLMs. Day to day: prompt engineering, fine-tuning models, building data pipelines, and applying MLOps (CI/CD, monitoring) in Python.
Staff ML engineer on Airbnb's Relevance and Personalization team, building end-to-end search ranking and recommendation models plus the pipelines and infrastructure behind them (training, serving, experimentation). Works with Python/Scala/Java/C++, TensorFlow/PyTorch, Spark, Kafka, Kubernetes, and Airflow at massive scale.
Customer-facing machine learning sales engineer at Lightning AI who acts as the technical partner to the go-to-market team — leading technical discovery, product demos, proof-of-concepts, and post-sales engagements to translate customer needs into AI/ML solutions on the Lightning AI platform. Requires 5-7 years in solutions/sales engineering; hybrid role based in NYC.
Machine learning engineer on Peloton's Personalization team, owning the end-to-end ML lifecycle for content recommendations: building ML pipelines, recommender and LLM-based models, and scalable low-latency microservices for real-time inference, plus running A/B tests. Core stack includes Python (and other JVM/systems languages), MLOps, and relational/non-relational databases.
Trinetix (posted via Internetwork Expert) seeks a Senior Python Developer in Warsaw to build scalable Python applications with AI/LLM integrations, design ML models, and manage ETL pipelines on AWS and Azure for a US client in consulting and finance. Core stack: Python, Pandas, AWS, Azure.
Junior ML engineer builds and tunes AI models for tax document automation, using Python, PyTorch/TensorFlow, and Azure AI tools in Irvine, CA.
Senior ML engineer on Carbon Mapper's Data Operations team builds and deploys deep learning models for satellite remote-sensing imagery (methane/CO2 plume detection, infrastructure mapping) while ensuring data products meet quality, latency, and cost targets. Core stack: Python scientific stack, PyTorch/TensorFlow, cloud ML pipelines and MLOps.
Own the full ML lifecycle for search relevance at ClickUp: training, deploying, and serving ranking models, building hybrid lexical + vector retrieval (HNSW, embeddings at scale), and improving query understanding in a permissions-aware multi-tenant search platform.
Remote Machine Learning Engineer (posted for San Diego, CA) with AbbVie/Allergan Aesthetics: owns ML components end to end — building data pipelines, training and evaluating models, and deploying them to production as microservices, APIs, batch or streaming jobs using Python, AWS, and MLOps tooling.
Machine Learning Engineer on Amgen's AI Studio (Applied AI) team, independently owning production components of enterprise AI products — Python/SQL services, GenAI/RAG/agent components, and data pipelines — from design and evaluation through cloud deployment, monitoring, and support in a regulated life-sciences environment. Remote role based in the U.S.
Develop and deploy AI models for pathology, collaborating with scientists and engineers to improve cancer diagnosis and treatment using machine learning and computer vision.
Machine Learning Engineer at EarnIn (fintech, earned wage access) training, deploying, and owning models for user-facing financial products — from predictive models over transaction data to agentic LLM applications. Core stack: Python, PyTorch, Spark/Databricks, LLM APIs. Hybrid in Mountain View, 2 days/week in office.
Machine Learning Engineer on LILA Sciences' Applied AI team, post-training and adapting the company's scientific AI models (SFT, RLHF/DPO/PPO/GRPO) to customer workflows, building evaluation loops, and moving model capabilities into production. Core stack is Python with PyTorch/JAX/TensorFlow and LLM/multi-modal/agentic systems.
Senior Machine Learning Scientist in drug discovery, building AI models to predict drug efficacy and toxicity using Python, PyTorch, and cheminformatics tools.
Machine Learning Engineer at PathAI who designs, develops, and deploys ML models for AI-powered pathology products and services. Works cross-functionally with biomedical data science, MLOps, and platform teams using Python, ML frameworks, and data pipelines.
Freeform seeks a Principal ML Researcher to lead machine learning for its AI-native metal 3D printing factories, building hybrid physics-ML models from petabyte-scale in-situ sensor data and deploying them into real-time closed-loop control. Core stack is Python plus C/C++, with physics simulation, digital twins, and controls.
Senior ML engineer on Apple's Siri Speech Evaluation team who owns the datasets, metrics, and automated judges used to evaluate speech LLMs (ASR, TTS, real-time conversational models) for accuracy, robustness, and conversational quality before they ship. Core stack is Python, large-scale data pipelines (e.g., Spark), and LLM/human evaluation methods.
Machine learning engineer on Apple's Foundation Models team in Cary, NC, post-training large language models into intelligent assistants for Apple products. Day-to-day involves designing RL and preference-optimization strategies, building data pipelines, and creating evaluation methodologies using Python with JAX or PyTorch.
Staff-level individual contributor ML scientist at Monzo leading the design and shipping of advanced real-time fraud and financial crime detection systems using deep learning, graph and sequence-based models over billions of rows of data. Works in Python and SQL daily, mentors other ML practitioners, and steers FinCrime ML strategy with senior stakeholders.
Lead independent, hands-on validation of Monzo's credit decisioning models — including ML scorecards, origination PD models and NPV/unit economics models — while overseeing broader credit risk models (IFRS9, stress testing). Heavy use of Python and SQL for statistical analysis, challenger models, and strengthening the Model Risk Framework.
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