51 to 75 of 346 Permanent TensorFlow Jobs

neural networks, logistic regression, random forests). Proficiency with Java and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/Gen AI frameworks like TensorFlow or PyTorch. Experience with cloud platforms such as AWS SageMaker or Azure Machine Learning. Ability to translate business problems into solutions. Strong communication skills; bilingualism/multilingualism is a More ❯

and associated frameworks. High-growth Experience: Prior experience working in high-growth environments, ideally start-ups or scale-ups Coding Skills: Proficient in Python, SQL, and one of Pytorch, Tensorflow, Scikit-learn, with daily experience in writing, debugging, and optimising code. ML Ops Knowledge: Familiarity with tools like MLflow, Kubeflow, or Vertex AI, and experience implementing CI/CD More ❯

techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, ResNets, VAEs, GANs, Markov chains, etc. Experience using specialized machine learning libraries eg. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of using More ❯

technical audiences Ideally you have: • Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Technical Product Manager • Familiarity with AI frameworks such as PyTorch or TensorFlow • Contributions to open-source projects, particularly in the space of DevOps or AI Benefits We have local offices in Paris, London, Marseille and Singapore. France Competitive cash salary and More ❯

Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization More ❯

CAP theorem/concept). A keen eye for good user interface design and user experience. Experience with Python packages used for data science (familiarity with Pandas, Numpy, Scikit, Tensorflow). Knowledge of containerisation technologies (Docker, Kubernetes). What you'll do As a Full Stack Engineer, you will build and support well-designed, tested, and scalable web applications More ❯

video,etc. Knowledge of machine learning modelling techniquesand how to fine-tunethosemodelseg.XGBoost, Deep Neural Networks, Transformers,ResNets,VAEs, GANs,Markov chains, etc. Experience using specialized machine learning librarieseg.Fastai,Keras,Tensorflow,pytorch, sci-kit learn,huggingface,etc. Must demonstrate the capacity of reading, understanding and implementingnew techniques in the field of machine learning as they emerge. Experience of using Cloud More ❯

machine learning or engineering roles. Strong expertise in Machine Learning and Deep Learning, with exposure to Reinforcement Learning as a plus. Proficiency in Python and modern ML libraries (e.g., TensorFlow, PyTorch, JAX, or Keras). Experience with version control systems (GitHub, GitLab) and knowledge of clean, maintainable coding practices. Familiarity with CI/CD pipelines for automating ML workflows. More ❯

year hands-on experience in AI or ML in a professional environment (3-5 years preferred) • Strong knowledge of machine learning model development, deployment, and modern ML libraries (TensorFlow, PyTorch, scikit-learn, etc.) • Solid programming background with experience using statistical and signal analysis libraries • Experience with neural network architectures including deep learning models • Understanding of transformer architectures and attention More ❯

Education: BS in Computer Science, Engineering, Mathematics, or related field with relevant coursework in machine learning/statistics, software engineering principles, and database systems Core Skills: Python, ML frameworks (TensorFlow/PyTorch/scikit-learn), SQL, distributed computing, version control Experience: 1+ years in ML engineering or data engineering Mindset: Self-motivated with a growth mindset, adaptable to fast More ❯

ICML, ICLR, ACL, EMNLP, NAACL, ICLR with specific focus on agent systems, tool use, or multi-agent coordination. Familiarity with one or more deep learning frameworks (e.g. pytorch, jax, tensorflow, ) Experience in ML Research beyond completing a PhD (e.g. supervision, industry experience, leading academic initiatives, ). Excellent communication skills to report and present research findings and developments clearly, both More ❯

NeurIPS, ICML, ICLR, ACL,EMNLP, NAACL, ICLR) with specific focus on agent systems, tool use, or multi-agentcoordination Familiarity with one or more deep learning frameworks (e.g. pytorch, jax, tensorflow, ) Excellent communication skills to report and present research findings anddevelopments clearly, both orally and in writing. Curious and innovative disposition capable of devising novel, well-founded algorithmicsolutions to relevant More ❯

following essential skills and qualifications: Hold an active Security clearance (Ideally Top Secret with SCI Poly) Strong programming skills in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, or scikit-learn Proficient in data manipulation, feature engineering, and model evaluation techniques Solid understanding of different machine learning algorithms and their applications Experience deploying machine learning models More ❯

Learning, Computer Science, Engineering, Statistics, or equivalent fields • Strong mathematical background (linear algebra, calculus, probability & statistics) • Experience with machine learning model training and analysis through open-source frameworks (Pytorch, Tensorflow, Sklearn) • Experience crafting, conducting, analyzing, and interpreting experiments and investigations. • Experience with modern software development tools and practices (Git, pull requests) • Experience analyzing model performance with relevant metrics and More ❯

learning engineers, AI researchers, and data scientists, driving end-to-end AI solution delivery. Designing and implementing AI architectures across cloud and on-prem environments, leveraging tools like PyTorch, TensorFlow, and Hugging Face. Spearheading the development of production ready AI models, from foundational LLMs to custom built cognitive agents, solving real world business challenges. Establishing scalable ML/AI More ❯

cloud-native tools and services, such as Infrastructure-as-Code, Azure Monitor, Advance Threat Detection, Defender etc. Integrate and deploy AI and machine learning models and frameworks, such as TensorFlow, PyTorch, MXNet, etc., using cloud-based platforms and services, such as Azure OpenAI Service, AI Search, Machine Learning Services, etc. Introduce CI/CD pipelines that provide rapid feedback More ❯

or similar - or equivalent work experience (PhD in STEM subject desirable) 2+ years experience writing production-level code for computer vision based applications with Python ML libraries, e.g Pytorch, TensorFlow Proficiency with version control and cloud computing e.g. AWS, Azure Enthusiasm for complex problem solving Strong technical communication skills, including the ability to clearly disseminate new ideas and ML More ❯

or similar - or equivalent work experience (PhD in STEM subject desirable) 2+ years experience writing production-level code for computer vision based applications with Python ML libraries, e.g Pytorch, TensorFlow Proficiency with version control and cloud computing e.g. AWS, Azure Enthusiasm for complex problem solving Strong technical communication skills, including the ability to clearly disseminate new ideas and ML More ❯

a Level 6 or Level 7 AI/ML/Data Science apprenticeship programme. Core Skills & Competencies Technical Skills Programming proficiency in Python and common ML libraries such as TensorFlow, PyTorch, or similar. Experience with Jupyter Notebooks and version control (Git/GitHub). Basic understanding of supervised/unsupervised learning, neural networks, or clustering. Analytical Abilities Ability to More ❯

Claude, Gemini, Llama, Falcon, Mistral. Model performance and optimization: Fine-tuning and optimizing LLMs for quality, latency, sustainability, and cost. Programming and NLP tools: Advanced Python, frameworks like PyTorch, TensorFlow, Hugging Face, LangChain. MLOps and deployment: Docker, Kubernetes, Azure ML Studio, MLFlow. Cloud and AI infrastructure: Experience with Azure Cloud for scalable deployment. Databases and data platforms: SQL, NoSQL More ❯

machine learning - Experience in building machine learning models for business application - Experience in applied research PREFERRED QUALIFICATIONS - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. - PhD Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce More ❯

streaming responses and cancelable endpoints to support real-time and interactive use cases. Preferred skills and experience Machine learning experience : Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn. Natural Language Processing (NLP) : Experience with NLP techniques and tools, such as spaCy or NLTK. Distributed systems : Knowledge of distributed systems and experience with tools More ❯

in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised More ❯

Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised More ❯

Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised More ❯