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What to Look for When Hiring a Machine Learning Engineer - When hiring an ML Engineer, recognizing specific attributes will ensure your new hire is capable and adds value to your team. Here are some skills and qualities to look for when hiring Machine Learning Engineers: https://hubs.li/Q02DLts30

Are you already bored by Large Learning Models and want to move a step ahead ? This is not just a job, it's an opportunity to shape the future, pushing the boundaries of what's possible and redefining the limits of computation and artificial intelligence

Get in touch! Machine Learning Research Engineer, System Infrastructure - #Midlothian. Interested? Click the link to apply #TechnologyRecruitment #3DReconstruction #Graphics #MachineLearning #NLP #DataEngineering #DataScience #Software #ImageProcessing #AI #TechnologyJobs #Robotics #ICResources #TechnologyCareers #DeepLearning #ArtificialIntelligence #ComputerVision

"🤔 Confused about the differences between Software Engineering, IT, Computer Science, Data Science, and AI? Check out this handy table to understand the focus, key skills, and typical job positions in each field! 🌟 Whether you're coding, managing networks, diving into algorithms, crunching data, or creating intelligent systems, there's a perfect tech career path for everyone! 🚀 #TechCareers #SoftwareEngineering #IT #ComputerScience #DataScience #AI"

Software engineer, data scientist, security architect... These are just a few of the exciting AI-focused career paths open to experienced computer science professionals. Explore 5 diverse roles that leverage your skills and passion for technology in this rapidly growing field. #computerscience #techindustry #careerchange #AI

Hello LinkedIn Community, I am excited to share further progress on our collaborative project with Pole Star Global: "Supervised Learning Approach for Ship Location Prediction to Predetermine Vessel Anomalies." This initiative, under the expert guidance of my supervisor Yanchun Bao at the University of Essex, continues to push the boundaries of what machine learning can achieve in the maritime industry. Since my last update, our team has made significant strides in enhancing the precision of our predictive models. We have successfully integrated new data sources, which has allowed us to refine our algorithms and improve real-time ship location predictions even further. This advancement not only bolsters the reliability of our anomaly detection system but also enhances the overall safety and efficiency of maritime operations. A key area of focus has been on improving the scalability and robustness of our machine-learning pipelines. By fine-tuning hyperparameters and experimenting with ensemble methods, we have been able to achieve substantial gains in model performance. This has been critical in ensuring that our solutions are not just accurate but also adaptable to the dynamic and complex nature of maritime environments. As I continue to explore the intersection of machine learning and maritime safety, I am increasingly motivated by the potential impact our work can have on the industry. The ability to predict and mitigate risks before they manifest is a game-changer for maritime operations, and I am proud to contribute to this cutting-edge project. In parallel with this exciting work, I am actively seeking full-time opportunities in data science, data engineering, or machine learning. I am eager to bring my skills and experience to a team that is passionate about leveraging technology to solve real-world challenges, whether in the maritime industry or other domains. I am enthusiastic about connecting with like-minded professionals and exploring potential collaborations or opportunities. Please feel free to reach out if you want to discuss how we can work together to drive innovation and make a meaningful impact. Thank you for your ongoing support and interest in our journey. I look forward to continuing to share our progress with you. Best regards, Sushma #DataScience #MachineLearning #DataEngineering #MaritimeIndustry #SupervisedLearning #AI #Innovation #TechCareers #Python #JobSearch #CareerGrowth #DataAnalysis #SoftwareEngineering #BigData #DataCleaning #DataPreprocessing #Analytics #HiringInTech #BigTechJobs #DataScientist #TechOpportunities #STEMCareers #DigitalCareers #TechJobsUK #JobsInData #TechHiring #TechJobsLondon

Which Job: Machine Learning Engineer or Data Engineer? I was asked by Manas Trivedi to explain the difference between these two positions and recommend which is best for a job search. Machine Learning Engineer has a sexy factor to it for sure, but that makes me suspicious. For one thing, it’s very specific to a certain kind of data processing and AI. We are already seeing this specialty getting crowded out by new hot specialties, such as “Prompt Engineer” and “RLHF Engineer”. Data Engineer can encompass all those sub specialties, and it is an industry recognized job title. Machine Learning Engineer sounds like a weasel phrase that can trade low pay for a hot title. Your mileage will vary from company to company, of course. Caveat: I’ve said before that MLOps is a legit skill set, and describes much of the real world skills companies need. With new AI tools such as LLM Apps this is absolutely true, but I think the title of MLOps Engineer will morph into something else regarding the LLM App pipelines in 2024 What do you guys think? What are some new titles that are worth putting into your job search? #ai #dataengineering #jobs #techjobs

I officially declare that I renamed my title here on LinkedIn from "Senior Machine Learning Engineer" to "Senior Machine Learning Engineer | Data Scientist". I stumbled, once again, on an official piece of product documentation stating that when a data scientist gives me his notebook to put in production, I, as a machine learning engineer, should do this and that, blah blah blah... I understand the confusion. A lot of companies have divided the responsibilities of creating smart ML-driven products into a data scientist that creates the model and a machine learning engineer that puts it into production. But there are also a lot of companies that did not make this distinction. In these companies (like my current or previous company) an ML engineer is responsible for an ML-driven product end to end. The ML engineer is responsible for the full spectrum from business requirements gathering and data acquisition/analysis to the model in production (with all the MLOps goodies one expects in a modern ML pipeline). He has to be knowledgeable in statistics, computer science, etc... ML engineer positions in the biggest tech companies (MAANG) follow this scheme. Of course, they also use a bunch of researchers who go much deeper into modeling. But still, their ML engineers are very good at pure data science. What about you? Are you an ML engineer also frustrated to see that some documentations state your job is solely dedicated to putting ML models in production?

One of the most annoying questions I get asked is what's the difference between an LLM Engineer / AI engineer v/s an LLMOps engineer. See, AI engineers are prompt engineers who now can do fine tuning and RAGs - don't have ML /DS background but can build LLM applications. The downside of that is since their core expertise is GenAI app development, their natural answer to every ML problem is a GenAI solution and that's the worst approach ever. Having a basic understanding of classical ML, RL, Bayesian methods, Deep Learning (aka ML) and then GenAI is imp so that you don't over-engineer hard solutions for easy problems. This is where I would personally hire LLM/ML engineers or AI Scientists that are keeping up with GenAI but have extensive experience knowing how to pick and choose the right solution for any problem. now the LLMOps engineer is like your Reliability Engineer in teams. think of it as - does your reliability Engineer need to be a software engineer? yes. very similarly, the LLMOps engineer must have expertise as an LLM/NLP/ML Engineer (depending on whatever your team calls it - I have seen all variations). But does the Reliability Engineer need to write the core product code? no, right! same, your LLMOps engineer's job isn't to build the models (even though they should be well versed or ideally have had experience in how to) but to make sure they are operating reliably in production. This means maintaining scalability, writing tests for robustness, integration tests, being on-call, looking at the tickets, automating and optimizing code pipelines. Everything a Site Reliability Engineer does for a software engineering team. does that make sense?

The Analytics, Decision, and Control Lab (ADC Lab) at UCF is hiring. Through generous support from NSF, ONR, AFRL, NASA, and DHS we are actively hiring for multiple post-doc (immediate) and grad student (summer/fall) positions. One project we are hiring for includes online monitoring and estimation of threat-levels towards government officials. Using NLP and Machine Learning techniques we are developing online monitors to estimate the threat-level facing government officials (esp. election officials). Related extensions of this homeland security research seeks to assess the threat-risk to other targeted groups and to map the online space of violent anti-government groups and violent hate groups. Those with a strengths/background in NLP should apply; understanding of US politics and extremism is a bonus. (This is a high-exposure project great for a post-doc with interests in NLP and homeland security) Others include (see other posts): * Optimal teaming of human-robot systems * Cognitive monitors for human-AI systems in aviation If you are interested in one of these positions please email adan.vela@ucf.edu with a copy of your resume and unofficial transcripts. Or if you know someone that might be interested in a position please share (like and share!). Traditionally successful researchers have a background in controls and dynamic systems, operations research, machine learning, and/or applied math & probability/statistics. As an interdisciplinary lab we welcome candidates form across engineering, math/prob, and CS disciplines. For those applying to Post-doc positions with the end-goal of an academic posting our team of faculty will actively support you in this endeavor through a "post-doc first" posture, while actively facilitating connections and meaningful partnerships with our CoPIs at Gatech, Nebraska, Temple, and elsewhere. Support does not just come from project PIs but the whole IEMS department - through such joint efforts we have successfully placed a number of post-docs in tenure-track faculty positions.