Design and implement in-house tools to automate data labelling pipelines, reducing reliance on manual annotation using Active Learning, Weak Supervision, and Synthetic Data Generation.
Responsibilities
Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
Develop 'Human-in-the-Loop' (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
Quality Assurance & Denoising: Implement algorithmic checks to identify and correct mislabelled or 'noisy' data within existing datasets.
Tooling & Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
Model Optimization: Fine-tune 'teacher' models to generate high-quality pseudo-labels for 'student' models.
We are looking for a Project Lead familiar with cloud-native and AI-native development practices to lead a team of engineers working with LLMs, agents, and retrieval systems.
Responsibilities
Develop intuitive AI-native software that meets and exceeds the needs of the company
Development of technical specifications and planning, including model selection, prompt/agent design, and data/retrieval architecture
Analyze and automate tests for software components prior to delivery, and build evaluation harnesses (accuracy, regression, and hallucination checks) for AI features
Troubleshoot coding and model-behavior problems quickly and efficiently to ensure a productive workplace
Follow secure software coding practices, including AI-specific safeguards (prompt-injection defense, data privacy, PII handling, and responsible-AI guardrails)
The ML Engineer will build, deploy, and maintain production-grade machine learning services and tooling in the advertising domain, focusing on MLOps and SRE principles.
Responsibilities
building, deploying and maintaining production-grade machine learning services and specialised tooling
designing and orchestrating ML engineering workflows, including model packaging, deployment strategies, observability and operational stability
productionising machine learning models in close collaboration with Data Science and Research teams
developing and maintaining automated deployment pipelines, model monitoring, alerting and rollback strategies
ensuring reproducible model packaging and reliable model lifecycle management
Lead the development and execution of value selling methodology for Google’s AI and Workspace products, influencing global sales and product teams to accelerate adoption and drive business impact.
Responsibilities
Develop and iterate on a value selling methodology across Google’s AI products
Collaborate with global sales, product, and customer stakeholders to craft compelling value propositions
Lead customer‑centric value pitches, business cases and TCO analysis
Leverage data, frameworks, models, and tools to justify and accelerate customer investment decisions
Brief Cloud sales and engineering leadership on customer patterns and emerging trends
Requirements
Bachelor’s degree or equivalent practical experience
Nice to have: value selling methodology
Nice to have: cloud sales
Nice to have: passion for AI
Nice to have: operating effectively in a fluid team environment
As an Artificial Intelligence (AI) Sales Specialist, you will help Google grow the AI business by building and expanding relationships with new and existing customers.
Responsibilities
Cultivate relationships with customers as a subject matter expert and trusted advisor, manage business cycles, identify solution use cases, and influence long-term direction of accounts.
Achieve quota and exceed business and growth goals while forecasting and reporting territory’s business.
Collaborate with Google accounts and cross-functional teams (e.g. Customer Engineering, Marketing, Customer Success, Product, Engineering, Channels) to develop Go-To-Market (GTM) strategies, drive pipeline and business growth, close agreements, understand the customer, and provide excellent prospect and customer experience.
Design and execute account plans informed by the responsible AI framework across an assigned territory or market.
Manage multiple customers and opportunities simultaneously, understand each customer’s technology footprint and strategy, growth plans, business drivers, and how they can transform their business using the technologies.
Requirements
Bachelor's degree or equivalent practical experience
Nice to have: carrying and exceeding business goals
Nice to have: supporting long-term executive relationships
Nice to have: developing new territories and accounts
Nice to have: planning and organizing solution-based business activity
This fellowship is designed for a researcher at the intersection of industry-scale data and academic rigor. The role involves leading research at the forefront of Document Understanding, designing and implementing AI-Native agentic systems to unlock insights from billions of historical and genealogical records.
Responsibilities
Innovate with State-of-the-Art AI for tasks such as OCR/HTR, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs.
Design and implement multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, AgentCore, Strands, Google ADK, A2A.
Evaluate the performance of multi-modal AI & LLMs such as GPT, Gemini, Claude, Llama, and Qwen for zero-shot and few-shot scenarios.
Lead cloud-native application design, development, testing, and modernization efforts across Google Cloud programs within PwC’s Advisory practice.
Compensation
The salary range for this position is: $155,000 - $410,000. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus.
Responsibilities
Setting strategic direction for Google Cloud-native engineering programs across AI-enabled application development and testing
Leading client discussions to shape solution roadmaps, delivery priorities, and modernization approaches for cloud-native products
Overseeing multiple software engineering efforts from requirements through architecture, build, test, and deployment
Driving development of generative AI solutions using Google Vertex AI, LLM APIs, LangChain, LangGraph, and retrieval-augmented generation
Governing prompt engineering, LLM evaluation, LLM guardrails, and LLMOps practices to support reliable solution behavior
Lead automated testing and MLOps quality for Hivemind autonomy platform.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Own technical strategy, architecture, and roadmap for automated testing, verification, and MLOps quality across the Hivemind ecosystem.
Design and maintain scalable test frameworks for autonomy software, backend services, APIs, operator-facing applications, and distributed hardware environments.
Lead functional, integration, regression, system, performance, reliability, and end-to-end testing across simulation, edge-compute, software-in-the-loop, and hardware-in-the-loop environments.
Build automated ML validation pipelines covering data quality, training reproducibility, model accuracy, robustness, regression, latency, resource utilization, and system integration.
Establish CI/CD and continuous training workflows that provide versioning and traceability for datasets, models, configurations, evaluation results, and deployment artifacts.
Requirements
Bachelor's degree in Computer Science or related field
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