דרושים » אלגוריתמים » Machine Learning Engineer

Why Join Us?
We are looking for a strong, hands-on Data Scientist to join our Data Loss Prevention team.

In this role, you will join an R&D team that researches, designs, and develops Check Points data loss prevention cloud services, providing state-of-the-art AI-based data loss prevention solutions on a global scale.

Check Point's data loss prevention solution is a platform used by all Check Point products, including Network Security, Email Security, SASE, Endpoint, SaaS, and Cloud Security.

In today's AI-driven world, data is an invaluable asset, and safeguarding it is paramount.

This role offers a unique opportunity to leverage extensive data collections and deliver transformative results in the Data Loss Prevention domain.

Key Responsibilities


Join the research and development of new innovative capabilities for data loss prevention, involving advanced AI and algorithms.
Quickly iterate on design approaches based on data-driven research and user feedback.
Design and develop novel data-driven solutions that have the potential to deliver game-changing results involving advanced AI and algorithms.
Utilize Natural Language Processing (NLP) and large language models to enhance data loss prevention capabilities.
Work on web content categorization as part of the broader scope of data protection.
Collaborate with Cyber Researchers, Analysts, Fellow programmers, and Data Scientists to deliver machine learning and analytics solutions.
Push the solutions all the way to large-scale production systems. Understand the architectural constraints of such systems and work with a cross-organizational engineering and product team to quickly transition from prototype to a scalable robust implementation.

Meta is seeking talented engineers to join our teams in building cutting-edge products that connect billions of people around the world. As a member of our team, you will have the opportunity to work on complex technical problems, build new features, and improve existing products across various platforms, including mobile devices and web applications. Our teams are constantly pushing the boundaries of user experience, and we're looking for passionate individuals who can help us advance the way people connect globally. If you're interested in joining a world-class team of engineers and researchers to work on exciting projects that have significant impact, we encourage you to apply.
Software Engineer, Machine Learning Responsibilities
Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences
Implement custom user interfaces using latest programming techniques and technologies
Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
Set direction and goals for teams, lead major initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
Architect efficient and scalable systems that drive complex applications
Identify and resolve performance and scalability issues, and drive large efforts to reduce technical debt
Work on a variety of coding languages and technologies
Establish ownership of components, features, or systems with expert end-to-end understanding

The financial risk management (FRM) machine learning principal will be the most senior machine learning engineer and strategist for financial risk at Meta. The principal will enable the risk organization to deliver significant lift over current long range objectives for friction and leakage through the generation of new Machine Learning opportunities for the organization and support of successful delivery of the risk management ML architecture. This person will partner closely with the FRM engineering leader (Director level) and be part of Metas risk management leadership circle.
Financial Risk Management Machine Learning Principal Responsibilities
Address core business and technical machine learning opportunities: elevate the existing portfolio of machine learning solutions to be state-of-the-art for minimizing Metas financial losses (due to leakage, good revenues loss and friction). Following are a few examples of technical and business problems we aim to address. - Provide a solution for optimizing the risk machine learning model ensemble (covering the entire end-to-end advertiser funnel including detection, decisioning, enforcement and remediation) through optimization of the current model portfolio and individual models. - Minimize the impact of the prolonged financial fraud feedback loop. - Improve models measurement and performance. - Optimize data/label strategy. - Optimize balance between specific targeted model strategy and broad umbrella model strategy to optimize for short and long term benefits
Lead Research and Introduction of Advanced Technologies: - Collaborate with Financial Integrity's senior ML Engineers to lead the research and introduction of deep learning and Large Language Model (LLM) technologies. - Remain current on industry-wide advancements in ML and introduce relevant advancements in Financial Risk Management
Collaborate on Next-Generation ML Architecture: - Work closely with financial harms principals and risk management tech leads to deliver the next-generation ML architecture for Meta's risk management system. - Collaborate with Principal ML engineers from across the company to adopt best industry and Meta practices within the FRM team. - Resolve or mitigate design dilemmas, balancing business and technical trade-offs. - Identify and initiate opportunities for collaboration and impact with other organizations at Meta
Identify and Initiate New Business Opportunities: - Collaborate with Meta FinTech, Central Integrity and Core Ads Growth partnerships to identify and initiate new business opportunities based on third-party capabilities. - Conduct proof of concept for different opportunities and initiate integrations to enhance business performance
Grow Other Senior ML Engineers - Actively invest in the growth of other senior ML engineers through goal-driven formal and informal mentorship. - Provide regular feedback to other engineers regarding their technical work

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.

Our team focuses on developing foundation models for remote sensing (i.e., satellite, aerial) imagery. We aim to simplify and enhance various remote sensing tasks including land use/land cover segmentation, object detection, and change detection for use by Google teams and external partners. We also investigate natural language interfaces for remote sensing imagery through vision-language models and tasks such as captioning and visual question answering.

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
Explore research directions, generate hypotheses and perform rigorous experimentation in several sub-domains (e.g., pre-training recipes, multi-modality, temporal sequences).
Contribute to shaping the team's roadmap, help prioritize research directions, with focus on innovation and usability.
Work with other researchers across Google to connect multiple geospatial efforts.
Help teams use foundation models and optimize its use on their tasks.
Advise and mentor team members.