לוח משרות דרושים NLP/Machine Learning

Similarweb is the leading digital intelligence platform used by over 3500 global customers. Our wide range of solutions power the digital strategies of companies like Google, eBay, and Adidas.

We help our customers succeed in todays digital world by giving them access to data-driven insights, competitive benchmarks, strategic analysis, and more. In 2021, we went public on the New York Stock Exchange, and we havent stopped growing since!

We are looking for a talented and dedicated ML & Big Data Analyst who will be a part of the Core Data group.

As part of this role, you will help us process and streamline rich and complicated datasets. Will explore and validate new data sources. Be part of the data science and data engineering force creating new features for our solution.

What does the day-to-day of a ML & Big Data Analyst at Similarweb look like:

Apply your expertise in quantitative analysis and data mining to turn data into insights.
Conduct research and develop tools that will ensure the quality of Similarwebs data and algorithms.
Utilize your deep understanding of Similarwebs data in order to evaluate and improve novel algorithms produced by the data scientists in your team.
Partner with Big Data Engineers to establish an infrastructure to scale solutions.

Similarweb empowers over 4300 global customers with data-driven insights and competitive benchmarks. We support the digital strategies of companies like Google, eBay, and Adidas. Similarweb went public on the NYSE in 2021 and continues to grow rapidly.

Role Summary:

We are looking for a Senior Software Engineer/ Machine Learning to join our team and help advance our Apps solution. Our product is designed to provide detailed and accurate insights into Apps Analytics, such as traffic estimation, revenue analysis, and app characterization. The role involves constructing and maintaining scalable data pipelines, developing and integrating machine learning models, and ensuring data integrity and efficiency. You will work closely with a diverse team of scientists, engineers, analysts, and collaborate with business and product stakeholders.

Key Responsibilities:

Develop and implement complex, innovative big data ML algorithms for new features, working in collaboration with data scientists and analysts.
Optimize and maintain end-to-end data pipelines using big data technologies to ensure efficiency and performance.
Monitor data pipelines to ensure data integrity and promptly troubleshoot any issues that arise.

Research happens at Google everyday, on many different embedded teams throughout the company. Our research reaches the user through both services and products such as Search, Maps, Google Assistant, Google Translate, Google Cloud, and our computing, storage, and networking infrastructure. To achieve this, were working on projects that utilize the latest technologies that push the boundaries of what is possible. At Google, research-focused engineering interns are embedded throughout the company, contributing to the setup of large-scale tests and deploying promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. From creating experiments and prototyping implementations to designing architectures, research-focused engineering interns work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and more.
Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities
Participate in research to develop solutions for real-world, large-scale problems.

We are looking for a skilled Product Manager to lead our ML efforts. This position plays a crucial role in steering the creation and implementation of our ML products, generating valuable insights that will influence our product roadmap, and uncovering opportunities. The perfect candidate will possess a solid expertise in data science, and product management, with a special emphasis on utilizing data-driven insights and machine learning models to deliver impactful products.

What you'll be doing

Have complete ownership of your products and oversee the entire lifecycle, from ideation to implementation
Shape the vision and roadmap for the products under your purview
Gather and analyze feedback from a wide range of stakeholders to refine requirements and identify opportunities to make sure your team is always working on the most impactful things
Work closely with Engineering, Data Science, business, and operational teams throughout the organization to ensure timely and top-notch product releases

Aura from Unity, a revolutionary large scale mobile platform, is looking for an outstanding and passionate machine learning engineer to join our team.

Join our team and you'll have an opportunity to research, design, implement, and evaluate a variety of advanced ML algorithms using the latest technology.

What you'll be doing

Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models
Transforming data science prototypes and applying appropriate ML algorithms and tools
Build a full ML lifecycle models including model monitoring capabilities
Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, were working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.

Responsibilities
Drive project work by defining the data structure, framework, design, and evaluation metrics for research solution development and implementation. Identify timelines and obtain resources needed.
Identify defined problems/gaps in existing technology and engage stakeholders and leaders to address them.
Lead the research of technology for improving Large Language Model (LLM) efficiency of performing target capabilities or supporting many capabilities, such as novel architectures and improved pre-training.
Collaborate with other Research teams to expand efficient LLM technology.
Collaborate with Google first-party partner teams to deliver new technologies to production.

Meta is embarking on the most transformative change to its business and technology in company history, and our Machine Learning Engineers are at the forefront of this evolution. By leading crucial projects and initiatives that have never been done before, you have an opportunity to help us advance the way people connect around the world.

The ideal candidate will have industry experience working on a range of recommendation, classification, and optimization problems. You will bring the ability to own the whole ML life cycle, define projects and drive excellence across teams. You will work alongside the worlds leading engineers and researchers to solve some of the most exciting and massive social data and prediction problems that exist on the web.
Software Engineer, Machine Learning Responsibilities
Play a critical role in setting the direction and goals for a sizable team, in terms of project impact, ML system design, and ML excellence
Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
Re-evaluate the tradeoffs of already shipped features/ML systems, and you are able to drive large efforts across multiple teams to reduce technical debt, designing from first principles when appropriate
Leading a team from a technical perspective to develop ML best practices and influence engineering culture
Be a go-to person to escalate the most complex online/production performance and evaluation issues, that require an in depth knowledge of how the machine learning system interacts with systems around it
Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models
Suggest, collect and synthesize requirements and create effective feature roadmap
Code deliverables in tandem with the engineering team

Meta is seeking a Research Engineer to join our Large Language Model (LLM) Research team. We conduct focused research and engineering to build state-of-the-art LLMs, which we often open-source, like our teams recent Llama 2. We are looking for strong engineers who have a background in generative AI and NLP, with experience in areas like language model evaluation; data processing for pre-training and fine-tuning; responsible LLMs; LLM alignment; reinforcement learning for language model tuning; efficient training and inference; and/or multilingual and multimodal modeling.
Research Engineer, Language - Generative AI Responsibilities
Design methods, tools, and infrastructure to push forward the state of the art in large language models.
Define research goals informed by practical engineering concerns.
Contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results.
Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).
Work with a large and globally distributed team.
Contribute to publications and open-sourcing efforts.

The Reality Labs team at Meta is looking for Senior/Lead Computer Vision Engineers to support our engineering teams as we build towards our goal of helping more people around the world come together and connect through world-class Augmented, Mixed and Virtual Reality hardware and software. With global departments dedicated to AR/VR research, computer vision, haptics, social interaction, and more, we are committed to driving the state-of-the-art forward through relentless innovation. AR and VR potential to change the world is immense -- and we're just getting started.

Meta is building the Metaverse, the "spatial internet" where immersive virtual worlds will coexist with the real world. Augmented and Mixed reality will transform the way people come together to interact, work and play. By developing new hardware and software products capable of understanding the real world and the user within their environment, we aim to make it possible for people to interact with content in their environment and share it with others.

Our Reality Labs division explores, develops and delivers cutting-edge technologies that serve as the foundations for the Metaverse and other future Reality Labs products, such as Oculus headsets, future AR glasses and our FB Family of Apps (Messenger, Instagram, WhatsApp). From Visual Localization, SLAM, 3D reconstruction, Context/Semantic Understanding, Mapping, Tracking, and Sensor Fusion, our team is focused on taking new technologies from early concept to the product level while iterating, prototyping, and realizing the human value and new experiences they open up.
Computer Vision Engineer (Leadership) - Reality Labs Responsibilities
Lead the design and development of novel computer vision and/or, machine learning algorithms in areas such as: real-time scene and object tracking, reconstruction and understanding, as well as, segmentation, face tracking, body tracking, key point estimation, depth sensing, generative approaches such as GANs, 3D stereo and volumetric reconstruction, avatars, reconstructions and virtual try-ons
Play a critical role in the definition and execution of long-term roadmaps in partnership and cross functional organizations in computer vision, machine learning, graphics, sensors, optics, and silicon
Lead and collaborate with multidisciplinary engineering and research teams to develop technologies from early exploration and incubation to production
Be a go-to person to escalate the most complex online / production performance and evaluation issues that require an in depth knowledge
Develop prototypes for future VR/AR/MR experiences, drive continued development, and integrate robust solutions into products
Participate in cutting edge research in computer vision that can be applied to AR/VR product development
Define projects for other engineers to possibly solve and achieve impact based on your direction

Central Applied Science (CAS) is a research and development team, working to improve Metas products, infrastructure, and processes. We generate real-world impact through a combination of scientific rigor and methodological innovation. Our focus is on longer-term, foundational work that addresses new opportunities and challenges across the Meta family of apps. The work we do enhances the Meta family of apps that enable billions of people to communicate with each other daily.

Central Applied Science is interdisciplinary, with expertise in computer science, statistics, machine learning, and economics among many other fields. This diversity of perspectives enriches our research and expands the scope and scale of projects we can address. We deliver value through collaborative projects with other groups at Meta and with the academic community. In addition, we build and open-source technical products aligned with our areas of expertise.

We are looking for research interns to join the Product Algorithms team in Central Applied Science. The team is an interdisciplinary team of quantitative scientists that aims to deliver research and innovation that fundamentally increase the magnitude of Metas successes. The ideal candidate will have extensive graduate training in computer science, machine learning, or related disciplines, and the passion to solve real world hard scientific problems. By applying your expertise and passion you will be empowered to drive impact across a range of products, infrastructure and operational use cases at Meta.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.
Research Scientist Intern, Central Applied Science (PhD) Responsibilities
Work with vast amounts of data, generate research questions that push the state-of-the-art, and build data-driven products.
Develop novel quantitative methods on top of Meta's unparalleled data infrastructure.
Work towards long-term ambitious research goals, while identifying intermediate milestones.
Communicate best practices in quantitative analysis to partners.
Work both independently and collaboratively with other scientists, engineers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value.