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Lead Software Engineer IV
Overview At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership…
ML Infrastructure Engineer
Build and optimize GPU-based ML infrastructure for large-scale AI systems, including training frameworks and data pipelines, while collaborating with ML teams to productionize models.
Summer 2027 | Software Engineer Intern (ML Research, Core Technology)
A summer 2027 ML research internship in Hong Kong on Nex's Core Technology team, developing and evaluating machine learning models (computer vision, audio, sensor fusion) that let the Nex Playground console perceive player movement in real time, with a focus on on-device, real-time inference. Open to enrolled undergrad/grad students with strong math and programming fundamentals.
Machine Learning Engineer, Platform Architecture
At Apple, our Platform Architecture group is responsible for connecting our hardware and software into one unified system. You’ll collaborate with engineers across Apple to design how our technologies work in unison,…
Principal Machine Learning Engineer, Ads & Promos Delivery
Lead AI-driven ranking and relevance systems for DoorDash’s ads marketplace, designing and deploying ML/LLM models to personalize ad delivery in real time across global markets.
Senior Staff Machine Learning Engineer, Ads
Leads AI-driven ads and promotions delivery systems at DoorDash, designing real-time ranking/relevance models using deep learning, LLMs, and sequence modeling to optimize merchant-consumer matching across search/discovery platforms.
Machine Learning Intern (Masters) - Summer 2027
A 12-week Summer 2027 machine learning research internship for Masters students at DoorDash. The intern conducts applied ML/AI research (ranking, recommender systems, computer vision, causal inference, ads) to improve discovery, ads, forecasting, fulfillment, and search, using Python or systems languages and ML frameworks like TensorFlow/PyTorch.
Machine Learning Intern (PhD) - Summer 2027
DoorDash is hiring PhD students for a 12-week Summer 2027 machine learning internship in San Francisco, Sunnyvale, NYC, or Seattle. Interns conduct applied ML research (recommender systems, ranking, ads, computer vision, causal inference) to improve DoorDash's discovery, ads, search, forecasting, and fulfillment experiences.
Senior Machine Learning Engineer, Drive
Senior ML Engineer on DoorDash's Drive team, owning end-to-end machine learning systems for delivery ETA, merchant prep-time, and order release prediction, plus logistics optimization and AI quality signals built with deep learning, reinforcement learning, and LLMs/VLMs. On-site in San Francisco, Sunnyvale, or Seattle.
Senior Machine Learning Engineer, New Verticals Agentic Foundations
Senior ML engineer on DoorDash's Agentic Foundations team building and improving LLM-powered agents (starting with the Ask grocery assistant) via an eval-driven loop — iterating on prompts, tools, and context, and fine-tuning/deploying small language models to balance quality, latency, and cost, while launching new agent experiences for merchants and dashers.
Senior Software Engineer, Machine Learning
Senior ML engineer on DoorDash's grocery/retail catalog team, designing, training, deploying and monitoring production machine learning models that keep the product knowledge graph accurate and semantically rich. Core stack includes PySpark, Snowflake SQL, PyTorch/scikit-learn-style frameworks, and AI coding tools like Claude Code and Cursor.
Staff Machine Learning Engineer, Personalization
Staff ML engineer building production personalization and growth models for DoorDash's consumer search across grocery and retail categories, partnering with product teams and mentoring juniors. Core work centers on Python ML (PyTorch/TensorFlow), causal inference, recommendation systems, and AI coding tools in a hybrid SF/Sunnyvale setup.
Staff Software Engineer, Machine Learning
Builds and ships machine-learning models and optimization algorithms (budget pacing, automated bidding) for DoorDash's ads platform and catalog/product knowledge graph, owning the end-to-end ML lifecycle from training through deployment, experimentation, and monitoring. Hybrid role using PyTorch, PySpark, and Snowflake.
Sr. Computer Vision Engineer
At OpenSpace, we’re redefining how the world’s most complex projects are built. Our AI-powered Visual Intelligence Platform uses computer vision and spatial AI to give construction teams a real-time view of what’s…
Senior Research Engineer
About Us is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone. Our mission is to facilitate breakthrough AI safety research, advance global understanding of AI risks…
ML Engineer: Build Scalable, Data-Driven ML Solutions
Machine Learning Engineer in the Netherlands who designs predictive models and manages a robust data foundation, building scalable ML applications deployed on clients' data platforms using Python and ML frameworks such as scikit-learn, PyTorch, and TensorFlow.
Data & AI Engineer Rotterdam
At Sytac, we build high-performing engineering teams for leading organizations in the Netherlands and beyond. We combine a pragmatic, people-first culture with strong technical craftsmanship — giving engineers autonomy…
Synthetic Data Engineer
Position: Synthetic Data Engineer Zorg ervoor dat u de volgende details zorgvuldig leest voordat u solliciteert. Location: The Hague - The Netherlands // Remote Are you an ambitious and talented engineer and do you…
Medior AI Engineer / Developer (Full-Stack)
Full-time (40 hours) or 32 hours | Eindhoven or Heerlen (hybrid) | 26 February 2026The Role As a Medior AI Engineer/Developer, you’ll turn prototypes and early-stage concepts into working, testable, and maintainable…
AI/ML Engineer
AI/ML Engineer working via outstaffer MWDN for a US PropTech client building a roofing platform. Day to day: develop and improve computer-vision systems for aerial roof image recognition — training, fine-tuning, and deploying models with Python/PyTorch — while prototyping new AI applications across the product.