51 to 75 of 778 Remote TensorFlow Jobs

with the analysis or application of data in finance, economics, or related fields is a plus. • Experience with machine learning libraries such as PyTorch, TensorFlow, JAX is a plus. • Experience applying generative AI solutions (via open source tools or through Azure, Amazon Bedrock, Nvidia etc.) is a plus. • Experience More ❯

responsible AI practices, including fairness, interpretability, and compliance. What We're Looking For Proven experience in machine learning model development and deployment (e.g., Python, TensorFlow, PyTorch, Scikit-learn). Solid grasp of ML concepts: supervised/unsupervised learning, NLP, computer vision, time-series, etc. Experience working with cloud platforms More ❯

responsible AI practices, including fairness, interpretability, and compliance. What We're Looking For Proven experience in machine learning model development and deployment (e.g., Python, TensorFlow, PyTorch, Scikit-learn). Solid grasp of ML concepts: supervised/unsupervised learning, NLP, computer vision, time-series, etc. Experience working with cloud platforms More ❯

objectives. Stay informed on advancements in AI and integrate the latest technologies into solutions. Key Skills & Experience: Strong programming skills in Python (experience with TensorFlow, PyTorch, or similar frameworks). Expertise in deploying models on cloud platforms (Azure) or containerised environments (Docker, Kubernetes). Proficiency in machine learning algorithms More ❯

computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for More ❯

computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for More ❯

in Bayesian inference and probabilistic modeling : PGMs, HMMs, GPs, MCMC, variational methods, EM algorithms, etc. Proficiency in Python (must) and familiarity with PyMC, NumPyro, TensorFlow Probability , or similar probabilistic programming tools. Hands-on experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods More ❯

NumPy, or Spark. At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure More ❯

NumPy, or Spark. At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure More ❯

Data Scientist with proven experience leading AI projects and mentoring junior team members. You should have strong expertise in Python, including libraries such as TensorFlow, PyTorch, and Hugging Face Transformers, with a solid understanding of LLM architectures. You should be well-versed in various machine learning techniques, including supervised More ❯

NumPy, or Spark. At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure More ❯

health care, etc Experience in using ML tools such as Mlflow for model lifecycle management Experience with common ML libraries and frameworks such as TensorFlow, PyTorch, Keras, Scikit-learn Familiarity with timely engineering and large language models (LLM) is a plus Proficiency in multiple programming languages Understanding of containerization More ❯

NumPy, or Spark. At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure More ❯

lead technical direction across projects or domains Expertise in model validation, explainability, governance and ethical AI principles Advanced proficiency in Python (e.g. Scikit-learn, TensorFlow/PyTorch) and SQL, plus familiarity with ML engineering and MLOps practices Nice to have: Experience in setting or influencing data science strategy in More ❯

Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the More ❯

/ML concepts applied to Digital Twin, with extensive hands-on experience in training and fine-tuning models using frameworks such as PyTorch or TensorFlow Proficiency with Generative AI tools such as GPT, DALL-E, Gemini, and a solid understanding of underlying Machine Learning pipelines Exceptional analytical and problem More ❯

Demonstrated experience working closely with business stakeholders in R&D, translating complex scientific challenges into AI solutions. Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face). Experience building and deploying models in cloud environments (e.g. Azure, GCP). Solid understanding of statistics, optimization, and More ❯

academic environment (RA/Internships are considered as work experience) Strong knowledge of Python, including core data science libraries such as Scikit-Learn, SciPy, TensorFlow, and PyTorch. Knowledge of software development good practices and collaboration tools, including git-based version control, python package management, and code reviews. Excellent communication More ❯

business problems. We prioritise clean, well-tested code with a culture of documentation and knowledge sharing. Our tech stack includes GCP, Python, GitHub, PyTorch, TensorFlow, Scikit-learn, and XGBoost. With mature infrastructure and dedicated teams for Data Engineering, Analytics, and Platform Engineering, our Data Scientists enjoy high autonomy. We More ❯

bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. More ❯

senior role. Proven experience in a startup or fast-paced environment. Strong proficiency in Python, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow/PyTorch). Experience working with financial data, risk modelling, or algorithmic trading is a plus. Familiarity with cloud platforms (AWS, GCP, or Azure More ❯

senior role. Proven experience in a startup or fast-paced environment. Strong proficiency in Python, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow/PyTorch). Experience working with financial data, risk modelling, or algorithmic trading is a plus. Familiarity with cloud platforms (AWS, GCP, or Azure More ❯

senior role. Proven experience in a startup or fast-paced environment. Strong proficiency in Python, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow/PyTorch). Experience working with financial data, risk modelling, or algorithmic trading is a plus. Familiarity with cloud platforms (AWS, GCP, or Azure More ❯

senior role. Proven experience in a startup or fast-paced environment. Strong proficiency in Python, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow/PyTorch). Experience working with financial data, risk modelling, or algorithmic trading is a plus. Familiarity with cloud platforms (AWS, GCP, or Azure More ❯

members. Essential Skills: 5+ years in AI/ML and data science development. Proficient in Python, R, and relevant ML frameworks/libraries (e.g., TensorFlow, PyTorch, scikit-learn). Strong grasp of LLMs, GenAI, and AI system architectures. Experienced with large-scale datasets and preprocessing pipelines. Skilled in statistical More ❯