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.

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.

Lead the development of an AI-agent platform that autonomously analyzes financial transactions, customer activity, cash flow, and risk signals to support FinTech operations. The system will use LLM-based agents, ML models, RAG, and real-time financial data to investigate anomalies, assess risk, generate financial insights, and recommend actions. Key Responsibilities: Define the AI-agent architecture, product roadmap, agent workflows, and evaluation strategy. Design specialized agents for fraud investigation, transaction analysis, risk assessment, cash-flow analysis, and financial reporting. Combine deterministic financial rules with ML predictions and LLM reasoning rather than relying solely on LLM outputs. Build agent orchestration using LangGraph/LangChain, tool calling, structured outputs, memory, and RAG. Develop ML pipelines for anomaly detection, behavioral scoring, transaction classification, and risk prediction. Implement human-in-the-loop approvals, confidence scoring, audit trails, and agent observability. Establish evaluation frameworks for agent accuracy, hallucination detection, tool-use reliability, and financial decision quality. Work with engineering teams to productionize agents using Python, FastAPI, PostgreSQL, AWS, Docker, and MLflow. Core Technologies: Python, PyTorch, Scikit-learn, XGBoost/LightGBM, LangGraph, LangGraph, LLMs, RAG, vector databases, PostgreSQL, FastAPI, AWS, Docker, MLflow, REST APIs, Databricks, and event-driven architectures. Core ML Libraries: - Deep Learning: TensorFlow, PyTorch, Lightning - Classical ML: Scikit-learn, XGBoost, LightGBM - NLP/LLMs: Hugging Face Transformers, spaCy - Hyperparameter Tuning: Optuna, Ray Tune Infrastructure & Tools: - Cloud: AWS/GCP/Azure (S3, BigQuery, Sagemaker) - MLOps: MLflow, Kubeflow, Prefect - Data: SQL, Pandas, PySpark, Dask - Deployment: Docker, Kubernetes, FastAPI Expected Outcome: A production-ready multi-agent FinTech intelligence system where AI agents investigate financial events, combine ML predictions with financial rules, retrieve supporting data, explain their reasoning, and route high-risk decisions to human reviewers. Skills Artificial Intelligence (AI) Machine Learning AI Agent Development RAG PyTorch Scikit-learn Databricks MLOps

I need help setting up Grok quickly so I can start using it without wasting time on unnecessary steps. The goal is to get a clear, efficient setup process that helps me learn the basics fast and focus on what matters. Please share a straightforward plan, guide me through the setup, and explain key concepts in simple terms. I want to avoid confusion and stay on track from the beginning.

We are seeking an AI and Machine Learning Engineer to develop and implement machine learning models for our projects. The role involves working on algorithm development, model training, and integration into existing systems. You will collaborate with our team to improve model performance and support ongoing AI initiatives. Experience with Windsurf, MATLAB and microcontroller programming is a plus. This is a part-time opportunity for someone who can contribute to our technical goals and help drive innovation.

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

The objective is to develop an AI model that detects and classifies Longitudinal and Transverse Cracking (LTC) in asphalt airfield pavements, in accordance with ASTM D5340-24. The model will use fused Digital Elevation Model (DEM) and imagery data to identify cracks, measure their extent, and assign a severity level (Low, Medium, or High) based on the standard's width and spalling criteria. The output will support the broader Pavement Condition Index (PCI) workflow used across the company's 85-model system. What you'll need to do: • Access and understand the provided DEM and imagery data, along with existing labels • Build a pipeline that fuses DEM and imagery to detect cracks and measure their length and width • Train a model to classify each detected crack as Low, Medium, or High severity per the ASTM thresholds • Apply the standard's exclusion rule so distresses are not double-counted where other cracking is already recorded • Validate the model's outputs against the team's verified ground truth and report accuracy • Deliver the trained model along with a short write-up of results and any limitations

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.

AI Architect & Autonomous Agent Engineer (Full-Time, US-Based) Own a Live Production Agent Fleet WHAT THIS IS I run a small, profitable company with an unusual amount of automation behind it. A fleet of autonomous AI agents runs our internal data operation unattended for roughly 12 hours a day, every day. It is real production infrastructure that the business depends on. This is not a "build me a chatbot" job and it is not greenfield. The system exists, it runs daily, and mistakes cost real money. Multiple independent pipelines run in parallel, each doing multi-stage automated research, each calling paid third-party APIs at several points, each with its own quality gates and delivery step. Tens of thousands of records have moved through it. I have been operating and extending this system myself. I need someone to own it so I can stop being the bottleneck. This is an architect role and a builder role at the same time. You will design the system AND write the code AND debug it at 6pm when an agent has done something confident and wrong. There is no team under you to hand it off to. If that split appeals to you, keep reading. I will describe the domain and the specifics on a call, under NDA. What I can tell you publicly is the engineering problem, which is below and is genuinely the interesting part. WHAT YOU WOULD OWN 1. ARCHITECTURE AND AGENT DESIGN - Own the overall design: how the pipelines fit together, where state lives, what runs where, and what happens when any piece fails - Build and maintain autonomous agents that run for hours without a human watching, using Claude Code and Codex - Design the guardrails: quality gates, fail-closed checks, regression tests,and audit trails so an agent cannot silently ship bad work - Debug agents that did the wrong thing confidently, which is the hard part 2. MULTI-DEVICE FLEET ORCHESTRATION - Scale from one machine to many machines running the same pipelines at once - Solve the coordination problems that come with that: shared claim and lock systems so two machines never do the same paid work twice, distributed state, race conditions, safe failure modes - Build the setup and sync tooling so a new machine can be onboarded quickly and every machine runs identical, current logic 3. INTEGRATIONS AND DATA PLUMBING - Cloud spreadsheets and file storage used as coordination and reporting layers across machines - Several third-party vendor APIs, some of them metered and billed per call - Reporting that a non-engineer can actually read and trust 4. QUALITY AND COST CONTROL - Every paid API call should be justified and never duplicated - Build measurement into the system so we know our unit cost and can improve it deliberately, not by guessing WHO THIS IS FOR You will do well here if: - You have shipped agentic systems that run unattended, not just prompts that work in a demo - You think like a systems engineer: idempotency, locking, retries, race conditions, failing closed, and knowing the difference between "it returned 200" and "it actually worked" - You are comfortable in Python, APIs, and the command line - You test your own work adversarially and assume your first answer is wrong - You can explain a technical tradeoff to me in plain language without making me feel stupid or hiding the risk - You are comfortable working on something you cannot put in a public portfolio You will not do well here if you need tickets written for you, if you have only worked on greenfield projects, if you want to architect without implementing, or if you are more excited about model choice than about whether the pipeline is correct at 2am with nobody watching. LOGISTICS - Full-time, long-term. This is an ownership role, not a one-off project. - US-based required. Significant overlap with US Eastern hours. - NDA before we get into specifics. HOW TO APPLY Skip the generic cover letter. I will read all of these and ignore anything that looks templated. Please answer these three questions: 1. Describe an autonomous system you built that ran without supervision. What broke, how did you find out, and what did you change so it could not happen again? 2. Two machines are running the same pipeline against a shared queue of work items. Each item costs money to process. How do you make sure no item is ever paid for twice, and what happens when one machine dies mid-task? 3. What is a mistake an AI agent made in something you built that you did not catch until it had already caused damage? Short and specific beats long and polished. If your answer to #2 is one paragraph and correct, you are ahead of most applicants.

I'm early in my career and self-taught, working on breaking into AI engineering, and I want one honest hour with someone actually in the trenches — not a sales pitch, not encouragement. Just a candid conversation, video on. What I'm after: your unvarnished read on how someone in my position actually breaks in, where the field is really headed, what companies are truly paying to have built or solved, and — if you were starting out today with what you know now — exactly what you'd do over the next 6 months. About me: no CS degree, no traditional pedigree — I've been learning by shipping real projects (I've built a couple of small live AI products end-to-end: RAG, an agent, automation). I know enough to ask good questions and I'll come prepared so we use the time well. I'd also love to show you one thing I built for 60 seconds of brutally honest feedback.