Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Staff Software Engineer, Machine Learning Performance

Google
London
1 year ago
Applications closed

Related Jobs

View all jobs

Staff Machine Learning Performance Engineer, Inference Optimisation

Machine Learning Engineer (Databricks)

Staff Data Scientist

Staff Machine Learning Engineer - Autonomy

Staff Machine Learning Engineer

Applied Machine Learning Engineer

Minimum qualifications: - Bachelor's degree or equivalent practical experience. - 8 years of experience in software development and with data structures/algorithms. - 5 years of experience with AI/ML algorithms and tools, LLMs or other multimodal foundation models, and natural language processing. - 5 years of experience in distributed development and large-scale data processing. - Experience coding in C++ or Python. Preferred qualifications: - Experience in performance analysis and optimization, including system architecture, performance modeling, or similar. - Experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. - Experience in a technical leadership role leading project teams and setting technical direction. - Experience debugging large model performance in training or serving (e.g., ML Framework like JAX, TF, PyTorch). - Experience in Graphics Processing Units (GPUs), TPUs or other hardware accelerators. - Experience in ML system development. Google's 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 Google's 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. In this role, you will collect and analyze profile data and provide expert-level visualizations and user actionable advice. You will serve high impact opportunities within Alphabet (e.g., improving Large Language Models (LLMs), guiding TPU chip co-design) and cross-industry supporting new frameworks and chips to run in different cloud environments. Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems. - Drive continuous improvements to the machine learning software/hardware stacks through providing insightful performance debugging. - Provide insights by summarizing different views of captured profile data such as trace timelines, memory usage, High-level Operations (HLO) profiles, Machine learning (ML) graph summaries. - Learn and build an intuitive understanding of existing data collection, analysis, and visualization workflows. - Support new, exciting ML paradigms such as horizontal scaling for upcoming Tensor Processing Unit (TPU) chips by making contributions across the JAX, compilers stack and analysis tools. - Partner with product area leads, cloud customers to understand model optimization use cases, drive cross-functional efforts to deliver on chip profiling requirements, and propose new hardware features. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See alsohttps://careers.google.com/eeo/andhttps://careers.google.com/jobs/dist/legal/OFCCPEEOPost.pdfIf you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form:https://goo.gl/forms/aBt6Pu71i1kzpLHe2.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.