Entry-Level Machine Learning Engineer Jobs

NVIDIA is building the next generation of AI and advanced data technologies for Physical AI. Our MLOps GT team designs large-scale data infrastructure, LLM models and agents powering Ground Truth (GT) generation, and end-to-end AI workflows for global automotive and robotic partners. We are looking for a motivated MLOps Physical AI Data Intern to help build, scale, and optimize this global data ecosystem while driving strong collaboration across engineering, operations, and research teams. At NVIDIA, interns are integral to real-world projects that push the boundaries of Physical AI. You’ll gain hands-on experience in global-scale data operations, work with exceptional engineers, scientists, and play a role in enabling next-generation autonomous driving systems across the world.

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NVIDIA is seeking a Deep Learning Performance Intern to join their team in Shanghai, China. This role focuses on accelerating deep learning across various platforms and applications, including automotive, robotics, and generative AI. The intern will contribute to deep learning modeling, performance optimization, projections, and analysis, with opportunities to optimize hardware and software architectures and make a significant impact.

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Handshake is seeking a Machine Learning Engineer I for the Growth Relevance team to help shape the evolution of careers in the AI economy. This role will focus on developing, deploying, and enhancing machine learning systems for lifecycle optimization, personalized notifications, and monetization strategies. You will work with cutting-edge ML infrastructure, including embedding-based retrieval, Graph Neural Networks, and multi-stage rankers, contributing to critical marketplace metrics and responsible AI practices.

$150k - $189k

At Mach9, ML Engineers build the perception models at the core of our AI-enabled CAD system, extracting 3D object and line features from dense LiDAR point clouds and imagery. This product-focused role involves working closely with customers to develop Computer Vision/Machine Learning pipelines and workflows that solve their problems. You will be responsible for shipping new features and product lines end-to-end, from data strategy and model training to evaluation. This position is ideal for early-career engineers who learn quickly, are curious, and find satisfaction in shipping impactful products.

$180k - $300k

NVIDIA is seeking outstanding Software Engineer Interns to contribute to the development of groundbreaking technologies for AI and deep learning kernel libraries. The team focuses on building core software that accelerates high-impact AI workloads on NVIDIA GPUs, with an emphasis on deep learning primitives, kernel libraries, and performance-critical GPU software. Interns will engage in the design, development, optimization, and delivery of software integral to NVIDIA's AI platform, with opportunities to work on low-level kernels, performance primitives, and efficient implementations for modern AI and deep learning workloads. This role involves contributing to GPU-accelerated deep learning primitives, attention kernel implementations, runtime components, code generation systems, and performance-critical infrastructure for advanced AI applications, collaborating with world-class engineers across various AI software domains.

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Together AI

As a Systems Research Engineer Intern specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. You will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems, and contribute to the co-design of efficient GPU architectures and programming models. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. This internship is based on-site at our San Francisco HQ, running through the Winter term from January to April.

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Together AI

As a Systems Research Engineer Intern specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. You will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems, and contribute to the co-design of efficient GPU architectures and programming models. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation.

$121k - $146k

NVIDIA is seeking a Performance Software Intern for Deep Learning Libraries. This role involves developing optimized code to accelerate linear algebra and deep learning operations on NVIDIA GPUs, contributing to the foundational software that powers AI advancements. The intern will focus on achieving peak GPU efficiency for current and future GPU generations, working with code at a low level, close to the GPU hardware. The team's work is exemplified by the open-source CUTLASS project, which demonstrates performant matrix multiplication on NVIDIA's Tensor Cores using CUDA.

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Gecko Robotics is seeking an AI/ML Engineering Intern to join their Engineering team for Summer 2027. This is a full-time, onsite internship where you will work on a defined project that turns inspection data into decisions, building models and the systems around them that run reliably on real customer data. This role focuses on building the systems that analyze data, rather than just the analysis itself. Candidates must be authorized to work in the United States due to government program requirements.

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Perplexity AI

This internship program offers a full-time, in-person opportunity in Berlin for 12 to 24 weeks. Interns will focus on advancing search quality through innovative models, data, and tools. The role involves training and optimizing large-scale deep learning models, with a particular emphasis on retrieval and ranking models. Additionally, interns will conduct research in representation learning, covering areas like contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval. A key aspect of the role includes building and optimizing RAG pipelines for enhanced grounding and answer generation.

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