transformer architectures to create intelligent conversational agents. Dive into the world of traditional NLP techniques and stay ahead of the curve. Apply a strong understanding of fundamental concepts-statistics, linearalgebra, calculus, regression, classification, and time series analysis - to extract valuable insights from data. Be the driving force behind our data visualisation efforts - whether its Tableau, Power BI … python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linearalgebra principles for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep More ❯
high-performance CUDA kernels for matrix operations and numerical solvers Profiling and optimizing GPU execution using NVIDIA tooling (e.g., qdss , Nsight Systems/Compute) Working with large-scale matrix algebra , linear equation solving, iterative solvers, and sparse/dense matrix handling Adapting existing CPU-based simulation code to GPU environments Ensuring numerical stability and precision in GPU-accelerated … handover of GPU-optimized modules Optional: contribution to Jetson-based environments if needed Required Skills Strong experience in CUDA development (custom kernels, memory management, warp optimization) Background in numerical linearalgebra , matrix operations, and solving systems of equations Experience with GPU-accelerated libraries such as: cuBLAS, cuSOLVER, cuSPARSE, Thrust , or similar Knowledge of NVIDIA debugging/profiling tools More ❯
to) the following course subjects. For a complete list of courses and course descriptions, please refer to the departmental link below. Mathematics Analytic Geometry Calculus I Calculus II College AlgebraLinearAlgebra Math for Management Multivariable Calculus Pre-Calculus Statistics Introduction to Statistics Other Mathematics or Statistics Course or Subject Areas (please specify in your cover letter More ❯
fields preferred. Minimum 2 years relevant work experience preferred. Excellent programming skills in Python. Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning). Strong mathematical background (linearalgebra, calculus, probability, and statistics). Experience with scalable ML (MapReduce, streaming). Ability to drive a project and work both independently and in a team. Smart, motivated More ❯
City of London, London, United Kingdom Hybrid/Remote Options
Forward Role
to-day, you'll: ? Deliver engaging teaching sessions covering the full ML lifecycle — data prep, model training, evaluation, deployment and monitoring at scale. ? Explain the maths behind ML models (linearalgebra, calculus, probability, stats) in an accessible, engaging way. ? Support learners throughout their apprenticeship journey alongside Learner Success Coaches. ? Contribute to content and product development — creating learning materials More ❯
about quality of experience over quantity. Programming Skills: Preferably extensive Python experience. Problem-Solving & Math: Outstanding problem-solving skills, with a creative and analytical mindset. Strong foundation in mathematics (linearalgebra, calculus, probability) and comfort with reading research papers when needed. Communication & Collaboration: Excellent communication skills. You can clearly articulate complex technical concepts to team members and actively More ❯
small-scale system and process improvements to enhance functionality and efficiency. Qualifications Preferred: Applied Machine Learning experience (regression, classification, supervised, and unsupervised learning ) with a strong mathematical foundation in linearalgebra , calculus , probability, and statistics. Experience in time-series data analysis , including cleansing and normalization , and experience with scalable Machine Learning ( MapReduce , streaming). Software development expertise in More ❯
Finance, Ads, or others). Production experience with Deep Learning models (training, fine-tuning, or deployment). Excellent software engineering skills in Python or C++. Strong analytical skills in linearalgebra, geometry, and probability. Experience with a deep learning framework, such as PyTorch, JAX, TensorFlow. Extra Credit Relevant industry experience with low-latency systems (prior work on self More ❯
security tactics. Post graduate degree and/or related certifications in Machine Learning or Artificial Intelligence. PhD or masters in AI/ML preferred. Strong understanding of probability theory, linearalgebra and calculus. Knowledge of current academic work in Adversarial attacks of LLMs. In-depth experience with exploiting OWASP LLM Top 10 application vulnerabilities, such as prompt injection More ❯
Experience writing production ready code Familiarity with recent developments and literature on Large Language Models and AI agents Knowledge of core machine learning concepts and ML related maths like linearalgebra, probability and statistics, multivariate calculus Strong communication and presentation skills (discussion, brainstorming ideas, technical research writing etc Nice-to-haves Experience in industrial research through industrial research More ❯
to significantly improve model inference and training speeds through low-level optimizations Ideal candidates will have: Knowledge of distributed inference systems for handling high-volume workloads Strong background in linearalgebra, optimization, and machine learning algorithms Experience with generative AI models (GANs, Diffusion Models, Transformers) Knowledge of hardware-aware neural architecture design Experience with high-performance computing (HPC More ❯
