Springtail AI’s Post

Opportunity

Okay okay okay, network are you ready ? 😃🥁 I'm about to share with you a very cool new opportunity 👏🏽 Are you passionate about cutting-edge technologies and pushing the boundaries of AI? 🤖 We’re on the lookout for a great talented and driven Machine Learning Engineer to join our great team! 🌟 What you’ll bring: ✅ 2+ years of experience with RecSys, NLP, or CV ML technologies. ✅ 4+ years of expertise in data structures, algorithms, and software engineering best practices. ✅ Hands-on experience with Generative AI (think text, images, and videos!). ✅ Skilled in Python, C/C++, or Java. ✅ Proven track record with distributed systems at scale, AI frameworks like LangChain, AutoGPT, and more! ✅ Passionate about LLMs and eager to dive into Prompt Engineering, Fine-Tuning, and beyond. Bonus points if you’ve led projects, mentored teams, or have a PhD 🎓. And bonus point number two, you get to work with mega talented people! 🧡 💡 If you’re ready to innovate and shape the future of AI, this is your moment 👉 Link to application is in the comment section and let’s create the next big thing! #AI #MachineLearning #GenerativeAI #TechCareers #Hiring #AIInnovation

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

https://lnkd.in/dguMSc9H Job Overview As a Senior Machine Learning Engineer II, you will be responsible for developing ML solutions to increase the safety, efficiency and sustainability of the physical operations. You will work closely with various engineering teams across ML, full-stack, firmware as well as cross functional partners to deliver core infrastructure, services, and optimizations. You should apply if: You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely. You want to build for scale: With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world’s physical operations. You are a life-long learner: We have ambitious goals. Every has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go. You believe customers are more than a number: engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes. You are a team player: Working on our Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win—together. In this role, you will: Build and improve the accuracy of ML models, including retraining and optimizing open-source models to solve Samsara-specific problems Work with petabyte-scale data from camera and sensor devices to develop new models Optimize models for inference on the backend and/or on edge devices Partner with hardware and full-stack teams to deploy model for optimal performance and cost Stay connected to industry and academic research and adopt novel technology that suits Samsara’s needs Collaborate with PM to translate customer needs to ML solutions Champion, role model, and embed cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices Minimum requirements for the role: BS or MS in Computer Science or other relevant field 4+ years experience as an Machine Learning Engineer, Applied Scientist, or similar role Strong proficiency in one or more common languages (e.g., C++, Golang, Java, Python, Scala) Proficiency with common ML tools (e.g., Spark, TensorFlow, PyTorch) Experience deploying and iteratively refining models using customer feedback loops . . . . . .

Any PhD graduates in my network interested in applying Deep Learning to Algorithmic Trading make sure to reach out to Luchele Mendes Brook to learn more about one of the most unique roles in the Deep Learning Research market right now.

🌟 Machine Learning Engineer Roles: What You Need to Shine 🌟 Seeking your dream ML job? It's not just about the qualifications! Your profile should radiate a dynamic skill set: 👨💻 Be fluent in Python, R, or Scala. 💡 Master algorithms for any learning type. 📊 Show off top-notch data handling abilities. 🤖 Have a firm grasp of neural networks and deep learning. 📈 Display real-world projects that solve problems. 👥 Prove you're a collaborative powerhouse. 📝 Convey complex ideas with simplicity and impact. Combine tech expertise with strategic problem-solving and collaborative energy to stand out. Practical, communicable skills will pave the way to your success! #MachineLearning #CareerTips

"🤔 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"

The truth is, even if you don’t fully grasp or appreciate ML at the algorithmic level, you can still succeed at the application level by building a great product. I love artificial intelligence, not just because it's trendy and reshaping our world, but primarily due to my decade-long fascination with machine learning algorithms. The fact that the first neural network algorithm was inspired by human brain networks continues to captivate me. Developing acoustical and lexical ML pipelines is my idea of fun. 😂 Considering the projections that the conversational intelligence market is expected to reach USD 34.7 billion by 2030, it's clear I'll be having a lot of fun. That being said, I am in the process of hiring a machine learning engineer. I am looking for an innovator, someone who is willing to push the limits, someone who is genuinely enthusiastic about the field. If you know of any such individuals, please refer them to me.

We're hiring a Principal Machine Learning Engineer. If you're passionate about AI and innovation and your skills match the job requirements, send me your resume. I'll review and refer top candidates. Make sure to mention the following details in the message, along with your resume: - First Name - Last Name - Email Address - Years of Experience Check the job description and requirements in the link mentioned in the comments. #MachineLearning #AI #Hiring #TechCareers

I think this is a smart statement and I have my own thoughts as well. When I was in the college, which was 10+ years ago, “ data science” or “big data” were the term people started talking about. Then other terms/professions became popular, such as “artificial intelligence”, “software development” , “blockchain” , “ bitcoin”, I think in 5-10 years, when we look back, LLM would defensively bring a surge of opportunities, even more opprtunites than we can imagine now. There are always some people who can’t catch those surges as well. For example, when I graduated, my job title was a “statistician”, because during that age, many people believe only PhDs are qualified for real “data science” jobs, if some master degree professionals claim they are “data scientists”, people will think they are bluffing. However, even after 5 years, nobody thinks like that. I feel it will be the same for this LLM trend. In 5-10 years, we will see all the data scientists be “LLM scientists”. So far, many Gen AI teams have a preference towards PhD candidates, and I totally believe in 10 years, it will not be like the same. But that also means, almost every candidate on the job market is going to have “LLM skills” on their resume. As a summary, if you can catch the trend, I’d say that would be helpful for your career life. But if you can’t quickly move into LLM/MLE/ML Ops area, I would say, don’t push too much on yourself, live well, live happily, work hard, always learn, and enjoy your life. And, always be positive about technology development, don’t be skeptical and believe it is just a gimmick.