Machine Learning Engineer: 21st October 2025

Beyond Founder Mode: Mission Mode (blog​.palantir​.com). Mission Mode organizes entire company around customer missions, embedding engineers with teams and prioritizing outcomes over founder involvement

How Meta Is Leveraging AI To Improve the Quality of Scope 3 Emission Estimates for IT Hardware (engineering​.fb​.com). Meta uses AI, NLP, LLMs (Llama 3.1) and PCFs to estimate IT hardware Scope 3 emissions, building a component-level taxonomy for data centers

Building Data Cloud’s New Unstructured Data Governance: Automated PII Detection at Enterprise Scale (engineering​.salesforce​.com). Automated PII detection across hundreds of gigabytes of unstructured data using Spark pipelines, Microsoft Presidio integration, and policy-driven masking

Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face (huggingface​.co). Google Cloud C4 with Intel Xeon Granite Rapids improves GPT OSS MoE inference throughput and 1.7x TCO savings over C3 via Intel-Hugging Face optimizations

Using AI to identify genetic variants in tumors with DeepSomatic (research​.google). DeepSomatic uses CNNs to identify somatic variants in tumor genomes across Illumina, PacBio, and ONT data with CASTLE training data

How to Get a Job in Edge AI: Essential Skills for 2025 (shawnhymel​.com). Edge AI on devices like iPhones, Android phones, Raspberry Pi, and microcontrollers; ML basics, Python/C/C++, Linux, RTOS, and hardware accelerators

Future-Proofing Your AI Engineering Career in 2026 (machinelearningmastery​.com). Future-proof AI engineering in 2026 via math foundations, system automation, cross-domain fluency, open source, and ethics

From Columns to Rewards: Automating the Two Pillars That Drive Modern AI (tomtunguz​.com). Reinforcement learning basics, feature engineering history, AutoML vs AutoRL, and the shift toward automated reward design

Engineering in the Age of Agents with Yechezkel Rabinovich (softwareengineeringdaily​.com). eBPF-powered observability with groundcover; BYOC model, kernel sensors, AI’s impact on code review and root-cause analysis

Data and AI culture: How Nu’s philosophy became a competitive advantage (building​.nubank​.com). Nu's data and AI culture, Data Mesh, autonomy, and 100+ ML models powering customer-centric decisions

Rising on arXiv - 2025-10-17 (blog​.rinesi​.com). Overview of high-bandwidth memory, causal attribution, and primordial non-Gaussianities in arXiv discussions and recent papers

Review: Genius Makers (blog​.piaw​.net). Biographical look at Geoff Hinton and students, tracing neural networks history and AI industry moves through Google and China

Cartography of generative AI (flowingdata​.com). Explores the groundwork of generative AI, including Estampa visuals, and the unseen data, energy, and people behind chatbot responses

Growing a 454-page ML reference manual in 5 days: permacomputer harvest (russell​.ballestrini​.net). Harvesting a 454-page ML reference manual in 5 days via permacomputer automation and multi-language seed implementations

ML quacks: Combining duckdb and mlpack (dirk​.eddelbuettel​.com). Combining duckdb with mlpack to run adaboost on rectangular datasets via a duckdb extension

Tracking Down Mysterious ML Training Stalls (medium​.com/@Pinterest_Engineering). Pinterest's ML training stalls traced to torch.compile interactions and a Ray monitoring task, resolved by removing psutils.memory_full_info

Integrating Python Forecasting with R’s Tidyverse (datageeek​.com). Integrates a Python forecasting model (nnetsauce MTS with BoosterRegressor) into R's tidyverse via reticulate for predictions and intervals

Decision Tree Regression (Without Recursion) From Scratch Using C# (jamesmccaffreyblog​.com). Decision Tree Regression (non-recursive) in C# with a handcrafted trainer and evaluation workflow

Saving a Trained Kernel Ridge Regression Model Using C# (jamesmccaffreyblog​.com). Kernel ridge regression with RBF kernel, Cholesky inverse training, and saving/loading model weights in C#

Dataflow Computing for AI Inference with Kunle Olukotun - #751 (twimlai​.com). Reconfigurable dataflow units for AI inference, multi-model serving, and dynamic hardware via Sambanova and Kunle Olukotun

Enabling Scalable AI-Driven Molecular Dynamics Simulations (developer​.nvidia​.com). Integrating PyTorch-based MLIPs with LAMMPS via ML-IAP-Kokkos for scalable, multi-GPU MD simulations and Python-based model loading

Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference (aws​.amazon​.com). Fine-tuning Amazon Nova Lite for document processing, leveraging Bedrock ODI, PEFT, and JSON structured outputs for tax forms

Configure and verify a distributed training cluster with AWS Deep Learning Containers on Amazon EKS (aws​.amazon​.com). Configure and verify a distributed training cluster on Amazon EKS using AWS DLCs with PyTorch, NCCL, EFA, FSx Lustre, and etcd/Kubeflow tools

Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face (huggingface​.co). Google Cloud C4 with Intel Xeon Granite Rapids improves GPT OSS MoE inference throughput and 1.7x TCO savings over C3 via Intel-Hugging Face optimizations

Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism (engineering​.fb​.com). Meta shares tensor, context, and expert parallelism innovations for scalable LLM inference and long-context handling

Scaling Large MoE Models with Wide Expert Parallelism on NVL72 Rack Scale Systems (developer​.nvidia​.com). Wide Expert Parallelism with NVL72 GB200, NVLink, and TensorRT-LLM boosts MoE inference throughput and lowers TCO

NVIDIA DGX Spark + Apple Mac Studio = 4x Faster LLM Inference with EXO 1.0 (simonwillison​.net). EXO 1.0: NVIDIA DGX Spark vs M3 Ultra Mac Studio for LLM prefill and decode; 4x faster inference on Llama-3.1 8B via streaming KV cache over 10Gb Ethernet

modded-nanogpt medium world record: Re-using intermediate activations in the output latents (snimu​.github​.io). Modded-nanogpt medium record reuses layer-11 activations in output latents with learned weights, exploring backout hypothesis and multi-layer skip experiments

Three AI customisation concepts (anna​.kiwi). Three AI customization concepts explained via embeddings, RAG vs fine-tuning, and LoRA with developer and non-technical metaphors

Writing an LLM from scratch, part 22 -- finally training our LLM! (gilesthomas​.com). Training an LLM from scratch, comparing GPT-2 weights, using AdamW, temperature and top-k sampling, and cost considerations

supplement to 0.430 (aarnphm​.xyz). DeepSeek MLA: multi-head latent attention, KV compression, and on-device serving with vLLM and RoPE-enhanced queries

The case for the return of fine-tuning (welovesota​.com). Fine-tuning resurges with LoRA, Tinker, PEFT, and open-weight ecosystems enabling modular, controlled AI with personal hardware touches

Introducing LightSHAP (lorentzen​.ch). LightSHAP: a lightweight, framework-agnostic SHAP implementation for tabular data with explain_tree and explain_any examples using CatBoost and linear models

How We Stress Test Credit Models (barnesanalytics​.com). Five-p pillar framework for stress-testing credit models (data checks, backtesting, macro scenarios, sensitivity tests, explainability and governance) with SHAP, calibration, and TPRM relevance

GAN-like Synthetic Data Generation Examples with DistroSimulator (thierrymoudiki​.github​.io). GAN-like synthetic data using DistroSimulator across univariate/multivariate distributions, digits, Fashion-MNIST, and Olivetti faces

Compositional modeling of plant communities with Dirichlet regression (ecogambler​.netlify​.app). Dirichlet regression with Gaussian process smooths for plant-community composition across elevation and temperature using brms and Hilbert-space GP approximations

Using AI to identify genetic variants in tumors with DeepSomatic (research​.google). DeepSomatic uses CNNs to identify somatic variants in tumor genomes across Illumina, PacBio, and ONT data with CASTLE training data

Whole-brain, bottom-up neuroscience: The time for it is now (thetransmitter​.org). Bottom-up whole-brain neuroscience using C. elegans, expansion microscopy, connectomics, optogenetics, and molecular modeling to build integrative brain models

How AI hears accents (accent-explorer​.boldvoice​.com). AI-driven 3D latent visualization of English accents using HuBERT fine-tuning and UMAP, revealing clustering by geography and immigration

Covariant spatio-temporal receptive fields for spiking neural networks (jepedersen​.dk). Covariant spatio-temporal receptive fields for spiking neural networks leveraging affine Gaussian derivatives and leaky integrator/IF neurons

Lecture - Theories of Deep Learning MT25, IV, Data classes for which DNNs can overcome the curse of dimensionality (ollybritton​.com). Overview of data classes where DNNs can beat the curse of dimensionality, including hidden manifold model, ReLU expressivity, and trajectory-based complexity

Geometric Structure of Emergent Misalignment: Evidence for Multiple Independent Directions (lesswrong​.com). Geometric analysis of six steering vectors across three emergent misalignment directions reveals orthogonal, independent mechanisms with asymmetric cross-laboratory transfer

Lecture - Theories of Deep Learning MT25, II, Why deep learning (ollybritton​.com). Overview of theories behind deep learning, architectures, and key papers shaping modern DL practice

“We can obtain less rigorous but more convincing results by other means” (new paper) (noncommutativeanalysis​.wordpress​.com). Experimental bounds for dilations of free unitaries; universal commuting dilation constant; semidefinite programming; Copilot; random unitary pairs

Linkage (11011110​.github​.io). Overview of geometric constructions, puzzles, and algorithms, including Herschel enneahedron truncation, origami links, cuckoo hashing, and Hermite interpolation in curves

H2O: Higher-Order Pattern-Discovery in High-Dimensional Data, Aarhus University, Denmark, March 30-31 2026 (appliedtopology​.org). Two-day workshop integrating TDA, TSA, and high-dimensional statistics on network data at Aarhus University

Distribution of coordinates on a sphere (johndcook​.com). Uniformly distributed sphere coordinates x, y, z show zero linear correlation but non-independence; uses normal samples, normalization, and distance correlation

PRISM: Probabilistic Runtime Insights and Scalable Performance Modeling for Large-Scale Distributed Training (arxiv:cs). PRISM models runtime variation in large-scale training using profiling and probabilistic simulation. Accurately predicts distributions, guiding parallelism, placement, and kernel optimizations for predictable, efficient clusters

Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning (arxiv:cs). Introduces MPC algorithms for multiplying secret sparse matrices, slashing memory and communication costs. Enables privacy-preserving ML on high-dimensional sparse data, with sparsity assumptions and applications

nuGPR: GPU-Accelerated Gaussian Process Regression with Iterative Algorithms and Low-Rank Approximations (arxiv:math). nuGPR combines preconditioned conjugate gradients, low-rank approximations, and CUDA parallelism to accelerate Gaussian Processes. Reduces training time and memory, making uncertainty-aware models practical for engineers

GRank: Towards Target-Aware and Streamlined Industrial Retrieval with a Generate-Rank Framework (arxiv:cs). GRank unifies target-aware candidate generation and ranking without structured indices. GPU-accelerated MIPS and training improve recall and throughput, simplifying industrial retrieval pipelines at billion-item scale

Dimension Mask Layer: Optimizing Embedding Efficiency for Scalable ID-based Models (arxiv:cs). DML learns per-feature embedding dimensions by masking components during training. Cuts embedding memory 40–50% with little loss, reducing overfitting and serving costs in ID-heavy models

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