We are a technology services firm supporting client projects in applied machine learning. We are looking for a senior machine learning engineer or applied scientist on contract to support model evaluation, training, and fine-tuning work. This is a hands-on role. We need someone with real machine learning fundamentals who has built, trained, and improved production models, rather than someone whose experience is primarily API integration. WHAT THE WORK INVOLVES - Designing and running model evaluation frameworks and benchmarks - Training and fine-tuning models for production use cases - Computer vision work including classification, detection, OCR, segmentation, and document or image understanding - Multimodal systems combining image and text or multiple data sources - Error analysis and iteration on underperforming models - Advising on dataset strategy, labeling approach, model selection, and experiment design BACKGROUND WE ARE LOOKING FOR - Machine Learning Engineer, Applied Scientist, or Research Engineer experience - Deep Python and strong PyTorch - Production experience with training pipelines and model evaluation - Ability to explain tradeoffs and recommendations clearly to client stakeholders ENGAGEMENT - Contract through Upwork with potential for ongoing work - Flexible hours with responsiveness expected during US business hours - Work performed under a subcontractor agreement covering confidentiality, IP assignment, and non-solicitation - We are only responding to independent contractors, not agencies. TO APPLY Send a short summary of your relevant work with links to GitHub, publications, or project writeups.

Looking for someone to build a model our company can train to find problem areas in agricultural imagery. Also someone who is willing to educate on machine learning/computer vision practices and techniques. Model should be trainable using supervised training. Areas we want it to identify have a geographic aspect, and are recognizable patterns. Expectation is to meet before project begins to review the desired deliverable, brainstorm on best approach, create model, train user on how to train the model, and provide support for further developments.

I’m looking for a Python/data science freelancer to help build a data pipeline and prediction model for UFC betting. The initial focus will be gathering, cleaning, and structuring historical UFC fight data and historical betting odds. If the engagement goes well, there is an opportunity to continue into feature engineering, machine learning, backtesting, model calibration, and bet-sizing. The ultimate goal is a system that estimates a fighter’s probability of winning and compares that probability against sportsbook odds to identify potentially mispriced bets. Initial Scope * Gather historical UFC fight and fighter data from reliable sources * Gather historical betting odds where available * Clean, normalize, and merge the datasets * Build point-in-time features using only information that would have been available before each fight * Prevent look-ahead/data leakage * Create a repeatable process for adding future UFC events and updating the dataset * Document data sources, assumptions, and methodology Potential later work includes below + reccomendations from the freelancer: * Logistic regression / Elo baseline models * XGBoost, LightGBM, or similar ML models * Probability calibration * Walk-forward historical backtesting * Comparison against no-vig sportsbook implied probabilities * Betting thresholds and bankroll sizing * Model diagnostics and performance tracking Important Requirement: Transferable System I am not looking for a black-box model that only the freelancer can operate. I have some prior programming experience but am rusty with Python, so clear code and practical documentation and handoff are important. All code, datasets, scripts, notebooks, documentation, and model files created during the project must be transferred to me. The system should be organized and documented so that I can understand the workflow, run the model myself, update it with new fight data, and continue improving it after the engagement. Ideal Candidate Experience with Python, pandas, data scraping/API integration, machine learning, time-series or sports-model backtesting, and probability calibration is preferred. Experience with sports betting or UFC data is a plus but not required. When applying, please briefly describe relevant projects you have completed, your proposed approach to the initial data-gathering phase, and an estimated cost or number of hours for that phase.

About Us We are an early-stage AI startup developing machine learning models that predict how different populations perceive and respond to fragrances and scented consumer products. Our goal is to combine chemistry, human sensory data, and artificial intelligence to help consumer goods companies better understand consumer preferences, accelerate product development, and make more informed formulation decisions before extensive physical testing. We work at the intersection of machine learning, computational chemistry, human perception, and behavioral science, tackling problems that require both strong engineering and scientific reasoning. About the Project We are currently working on a fast-paced project for a global consumer products company and are looking for an experienced ML engineer to join us for an initial two-week engagement. The work will involve building research prototypes, developing data pipelines, integrating scientific and chemical datasets, and helping create models that predict how different populations may respond to fragrance-related products. This is a highly collaborative role where you’ll work closely with our technical team to rapidly explore ideas, iterate on models, and solve challenging scientific problems. About the Role This is not a narrowly defined engineering position. As an early-stage startup, we’re looking for someone who is comfortable wearing multiple hats and contributing wherever they’re most valuable. Depending on the project, you may be involved in data scraping and preprocessing, scientific data integration, model implementation, feature engineering, exploratory analysis, and building tools to support ongoing research. Strong Python skills are essential, and experience with machine learning is expected. Do not apply to this role if you have no knowledge on neuroscience or chemistry.

We need a senior ML engineer to design and train a production-quality model from messy, real-world data. This is not a notebook demo. We need a well-reasoned model, a rigorous evaluation setup, and a clear record of what was tried and why. The problem We have a labeled dataset with class imbalance, noisy labels, and mixed feature types (structured fields plus free text). The goal is a model that generalizes on unseen data, not one that only looks good on a random split. What you’ll do Audit the dataset: leakage, label quality, missingness, outliers, and train/validation/test design Build a reproducible preprocessing and feature pipeline (structured features + text embeddings if useful) Train and compare several approaches (e.g. gradient boosting, linear baselines, and a neural/transformer option) Handle imbalance and noisy labels with the right sampling, loss, or calibration methods Run proper validation (stratified or grouped splits — no row-level leakage) Error analysis: where the model fails, which classes confuse, and what would improve it next Deliver trained artifacts, training/eval code, metrics, and a short technical write-up Success looks like A holdout evaluation you can defend (precision, recall, F1, PR-AUC, calibration) A baseline vs. final model comparison, not a single lucky run Code someone else can retrain without reverse-engineering your notebook What we’re looking for Senior experience building models in Python (PyTorch or TensorFlow, scikit-learn, XGBoost/LightGBM, pandas) Strong instincts on validation design, leakage, and overfitting Comfortable with both classical ML and deep learning; pick the simpler model if it wins Clear written communication and independent execution NLP / transformer experience is a plus To apply, send 1–2 relevant model-build case studies (what the data looked like, what you tried, and the final metrics) plus your rate.

I am looking for an AI and machine learning developer to help create a basic image-recognition application for Decathlon. The goal of the project is to build a model that can identify the difference between running shoes and soccer cleats based on uploaded images. The developer should have experience with artificial intelligence, machine learning, computer vision, image processing, Python, and TensorFlow. The project would include helping develop and train the image model, testing its accuracy, and creating a basic prototype that could potentially be used in a mobile app or on a website. The main deliverables would be a trained image-recognition model, testing results, and a basic working prototype. I am looking for someone with relevant experience, strong communication skills, and the ability to complete the project within the expected timeline and budget.

We are seeking an experienced trainer to deliver a corporate training on PySpark and Machine Learning for a group of professional participants. This training provides a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Participants should come away understanding how Apache Spark operates within modern Big Data ecosystems, how to efficiently process large datasets using distributed computing, and how to build end-to-end ML pipelines using Spark MLlib. What We're Looking For: •Demonstrated experience delivering PySpark and/or Machine Learning training to corporate groups •Strong hands-on facilitation skills — comfortable running lab-based exercises •Ability to explain both Spark architecture fundamentals and applied ML workflows •Own training materials preferred, or willingness to adapt a provided outline into a full curriculum Topics to Cover: •Spark architecture: driver, executors, cluster manager, lazy evaluation, DAG and execution planning •RDD vs. DataFrame APIs — differences and when to use each •Creating and configuring SparkSession •PySpark DataFrames: transformations, actions, column expressions, filtering, joins, aggregations •Data quality checks and writing reusable, maintainable PySpark code •Performance fundamentals: partitioning, shuffle behavior, caching, persistence •Optimization techniques: broadcast joins, execution plan analysis •Feature engineering with Spark MLlib: handling missing values, encoding categorical variables, feature scaling •Preparing datasets and building reusable preprocessing pipelines for Machine Learning •Training regression and classification models at scale (Linear Regression, Logistic •Regression, Decision Trees, Random Forest) •Comparing models and interpreting results in distributed ML workflows •Deploying Machine Learning workflows in production-oriented environments Basic Python knowledge, fundamentals of data analysis, basic SQL, and an introductory understanding of Machine Learning concepts.

Aurat is developing neuroscience-inspired AI models that predict how different human populations respond to scent. Our goal is to model the underlying neural and cognitive drivers of perception, emotion, and behavior from scent, enabling consumer insights without relying solely on traditional human panel testing. We are looking for a computational neuroscientist to help develop the next generation of our neural modeling platform. Responsibilities - Design and implement neuroscience-inspired machine learning models. - Develop neural architectures that predict population-specific responses to olfactory stimuli. - Build, train, and evaluate deep learning models using human perceptual datasets. - Design and execute experiments to improve model performance and generalization. - Explore novel modeling approaches, representation learning, and uncertainty estimation. - Work independently on open-ended research problems with minimal supervision. - Clearly communicate modeling decisions, assumptions, and experimental results. Required Qualifications - Computational neuroscience - Cognitive modeling - Strong background in machine learning and deep learning. - Strong Python programming skills. Preferred Qualifications Experience in one or more of the following areas: - Scientific machine learning - Representation learning - Bayesian modeling - Statistical learning - Brain-inspired AI - Sensory neuroscience - Psychophysics - Graph neural networks Ideal Candidate We're looking for someone who enjoys tackling novel scientific problems where there is no established solution. The ideal candidate is highly independent, comfortable reading scientific literature, capable of proposing new modeling approaches, and excited to rapidly iterate on experimental ideas. Contract Details - Remote - Full-time for two weeks - Immediate start - Potential for continued collaboration based on performance DO NOT APPLY IF YOU HAVE NO NEUROSCIENCE EXPERIENCE

What I Need Help With I have already set up my Raspberry Pi 5 and microscope camera. I am looking for someone to help implement the software pipeline for my project. Specifically, I need assistance with: Setting up and organizing the Python project structure. Use machine learning to be able to identify changes in morphology for each group over time (16 different experimental groups = 16 analysis folders to be compared to each other through ML) Building an automated workflow that processes microscope images using a custom-trained YOLO (Ultralytics) model. Running object detection on microscope images of Microcystis aeruginosa colonies. Extracting quantitative measurements from each detected colony, including colony count, area, equivalent diameter, circularity, and density. Automatically exporting all measurements into a CSV or Excel spreadsheet, with one row of data per image, and each piece of data being tracked based on which group it was taken from. Saving annotated images showing the YOLO detections for later review. Writing clean, modular, and well-documented Python code Troubleshooting any software or integration issues that arise during development.

Need an expert to build custom software that connects multiple AI tools and creates AI bots within those systems. The solution should support seamless integration, bot creation, and learning capabilities. Please share relevant experience with AI software development, bot creation, and system integration.