As a Machine Learning Scientist II, you will work within a cross-functional team of engineers and product managers to develop, evaluate, and deploy GenAI-powered solutions for scalable, customer-facing applications. Your work will focus on implementing agentic capabilities, contributing to evaluation frameworks, and delivering measurable business impact through data-driven experimentation.


Key Job Responsibilities and Duties:

Contribute to the design and development of end-to-end agentic systems, ensuring code quality and efficiency in production.

Build agentic solutions for different tasks and use cases using state-of-the-art techniques

Develop and carry out evaluation strategies, including formulating new metrics and building evaluation judges

Adhere to and promote established best practices in GenAI application development within the team.

Collaborate actively with team members, participating in code reviews, sharing knowledge, and contributing to a positive team environment.

Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into ML solutions.

Conduct deep data analysis to evaluate model performance, label quality, features exploration.

Work closely with ML engineers to ensure and improve the solutions latency/throughput meets product requirements and ensure deployment of your model to production.

As a Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.

Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.


Key Job Responsibilities and Duties:

Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.

Dealing with massive textual sources to train GenAI foundation models.

Solving issues with data and data pipelines, prioritizing based on customer impact.

End-to-end ownership of data quality in our core datasets and data pipelines.

Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.

Providing tools that improve Data Quality company-wide, specifically for ML scientists.

Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.

Acting as an intermediary for problems, with both technical and non-technical audiences.

Promote and drive impactful and innovative engineering solutions

Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.

Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.



Key Job Responsibilities and Duties:

Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.

Dealing with massive textual sources to train GenAI foundation models.

Solving issues with data and data pipelines, prioritizing based on customer impact.

End-to-end ownership of data quality in our core datasets and data pipelines.

Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.

Providing tools that improve Data Quality company-wide, specifically for ML scientists.

Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.

Acting as an intermediary for problems, with both technical and non-technical audiences.

Promote and drive impactful and innovative engineering solutions

Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

As a Senior Machine Learning Engineer, you will work closely with top notch engineers and data scientists to design, develop, evaluate and deploy Gen AI-powered solutions for scalable, customer-facing applications. Your work will focus on building and applying state-of-the-art agentic capabilities to drive business impact and improve efficiency.



Key Job Responsibilities and Duties:

Design, develop, and deploy high-quality, performant, and efficient Generative AI-powered solutions and agentic systems into production environments.

Evaluate and define optimal architectural solutions by considering emerging technologies, business needs, and technical requirements for latency, throughput, and scale.

Own services end-to-end, including implementing robust monitoring and maintenance strategies to ensure application and ML health, quality, and performance.

Write and maintain clean, scalable, and well-tested production code, ensuring reproducibility and seamless integration via CI/CD pipelines.

Pioneer and promote best practices and the adoption of cutting-edge technology in GenAI application development.

Collaborate effectively with Product Managers, Data Scientists, and Analysts to understand business requirements and translate them into technical ML and agentic solutions.

Provide technical guidance and mentorship to other engineers, contributing to the team's overall technical development.

As a Software Engineer Intern in the MSC IL AI Innovation team, you will contribute to the design, development, testing, and delivery of AI-powered features and services for our Specialized Clouds organization. You will work with mentors and engineering team members to build reliable, secure, and responsible software, while learning modern AI engineering practices across prompts, retrieval, agents, evaluation, observability, and cloud-native development.

This internship is designed for entrepreneurial, creative, and highly motivated students who are passionate about building AI-native products, taking ownership in ambiguous 0 to 1 environments, and using AI throughout the engineering lifecycle to solve complex, high-impact problems in sovereign and regulated cloud environments.


Responsibilities
Develop, test, debug, and maintain software components for AI-powered products, services, or internal tools.

Contribute production-quality code that is maintainable, secure, performant, and aligned with team standards.

Partner with mentors, engineers, product managers, and stakeholders to understand user requirements and translate them into engineering tasks.

Contribute to AI features such as agents, tool/API integrations, prompt flows, retrieval workflows, evaluation harnesses, and monitoring capabilities.

Help define and run tests, evaluations, and quality checks for AI behavior, including accuracy, relevance, groundedness, and reliability.

Investigate issues using logs, traces, test results, and debugging tools, and incorporate feedback into improved solutions.

Learn and apply engineering best practices across code quality, security, responsible AI, CI/CD, documentation, and observability.

Document work clearly and share implementation details with team members to support continuity and knowledge sharing.

Collaborate effectively with team members in a dynamic, ambiguous environment; adapt quickly across technologies, stacks, and engineering tasks; proactively seek and incorporate feedback; and contribute to shared goals while building technical depth.

Required Software Engineer III, Cloud Networking
About the job
Our software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to our needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
This Team owns the core data plane infrastructure for all NAT-based products within the Andromeda network stack (e.g., private service connect (PSC) and Cloud network address translation (NAT). These products serve as critical entry and exit gateways that securely connect Cloud customers to their services and networks.
Responsibilities
Design, develop, test, and maintain high-performance software for our Cloud's core networking and secure connectivity platforms.
Manage complex scalability, resource efficiency, and performance optimization challenges to evolve our cloud networking datapath.
Collaborate with cross-functional engineering partners to define and build new network architectures and routing methods.
Manage individual project priorities, deadlines, and deliverables, ensuring high engineering velocity and robust code quality.
Participate in operational rotations, monitor production health, and troubleshoot packet latency or connectivity issues to keep our global systems healthy.

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.



As a ML Engineer, you will:



Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.

Were looking for engineers who are proactive, curious, and take full ownership of their work. In this role, youll have the opportunity to drive real impact, shape the direction of our product, and grow as a technical leader.

You will:
Take end-to-end ownership of features-from design and implementation to deployment and ongoing improvement.
Build high-quality, scalable, and resilient systems using modern engineering practices.
Work closely with PM, design, and security experts to define and deliver customer-facing experiences.
Drive technical direction and make thoughtful tradeoffs that balance speed, quality, and long-term maintainability.
Bring a strong sense of initiative-identifying opportunities, raising issues early, and pushing ideas forward.

We are seeking a highly motivated Senior Deep Learning Researcher to join our team! This is an outstanding opportunity to conduct impactful research and develop the next generation of large language model (LLM) inference algorithms. You will work on technologies that directly enhance our software, making the latest LLMs more efficient and accessible for users worldwide.

By joining us, you will be part of a strategic effort to establish us as the definitive platform for high-performance LLM inference. You will engage with our skilled problem-solvers and top organizations, crafting AI technology advancements.

What you'll be doing:
Research, invent, and implement groundbreaking algorithms for LLM inference to advance the state of the art in both low-latency and high-throughput scenarios.

Translate research into practical software solutions that directly impact our products and customers.

Collaborate with internal research, engineering, and product teams across the globe to drive the development of advanced inference technologies.

Analyze the performance of new algorithms on our latest hardware, identifying bottlenecks and opportunities for algorithmic optimizations.

Partner with leading scientific organizations and industry pioneers to remain at the forefront of technological advancements and integrate the latest innovations into practical applications.

We are looking for a Software Development Engineer to own the design and implementation of our inference data plane. We build the software that makes large models run efficiently on custom hardware - spanning model execution, memory management, data movement, and serving integration.
Our work covers the full inference path: integrating serving engines with custom hardware, developing high-performance compute kernels, enabling efficient data movement, and driving models from early validation through production. We operate at frontier scale with large distributed models.
This is a ground-up effort with rapidly evolving hardware and software. We need an individual contributor who can write and optimize low-level code for custom hardware, validate model architectures end-to-end, build test and profiling infrastructure, and drive performance across the stack.

Key job responsibilities
- Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference.
- Implement and validate LLM architectures end-to-end - from PyTorch model definition through distributed execution on custom hardware.
- Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism.
- Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets.
- Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bringup.
- Own features end-to-end: from design through implementation, testing, and integration into the broader software stack.
- Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions.