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At Sonatus, we’re driving the transformation to AI-enabled software-defined vehicles. Traditional automotive software methods can’t keep pace with consumer expectations shaped by the mobile industry—where features evolve rapidly, update seamlessly, and improve continuously. That’s why leading OEMs trust Sonatus to accelerate this shift. Our technology is already in production across more than 8 million vehicles on the road today and rapidly expanding. Headquartered in Sunnyvale, CA, with 250+ employees worldwide, Sonatus combines the agility of a fast-growing company with the scale and impact of an established partner. Backed by strong funding and proven by global deployment, we’re solving some of the most interesting and complex challenges in the industry. Join us and help redefine what’s possible as we shape the future of mobility. The Opportunity: Sonatus is looking for an experienced Senior Engineering Manager to build and lead our AI Validation function — the team responsible for how we test, evaluate, and govern the AI models and agentic capabilities embedded in our software-defined vehicle and cloud platforms, as well as our cloud-only AI and LLM-based products. You will deeply understand how AI is developed and deployed across Sonatus’s embedded, cloud, and LLM/RAG-driven environments, identify gaps and friction in current validation practices, and turn those insights into scalable test strategy, evaluation frameworks, and governance mechanisms that let Sonatus ship trustworthy AI-driven features at automotive scale. You will lead and grow a high-performing AI validation team and partner closely with engineering, product, and safety stakeholders to make AI quality and safety a competitive advantage. Role and Responsibility: Define and drive Sonatus's AI validation strategy across embedded, in-vehicle, cloud-connected, and cloud-native AI systems, identifying gaps in model development, testing, deployment, and governance. Lead, hire, mentor, and grow a high-performing AI Validation organization, establishing scalable engineering processes, technical direction, and execution excellence. Own the end-to-end validation strategy for AI/ML models, LLMs, RAG pipelines, and agentic AI workflows—from data pipelines and model training through cloud services and in-vehicle deployment. Architect and operationalize scalable evaluation frameworks and benchmarking platforms for AI systems, including multi-step agentic workflows, using deterministic metrics, LLM-as-a-Judge methodologies, automated regression testing, and production feedback loops. Design and maintain evaluation harnesses for RAG and agentic systems that measure retrieval quality, grounding, citation accuracy, factual consistency, context relevance, safety, latency, reliability, and execution correctness. Evaluate, integrate, and optimize open-source and commercial AI validation technologies, driving build-versus-buy decisions for Sonatus's AI quality platform. Establish AI governance, Responsible AI practices, model lineage, safety guardrails, and compliance processes appropriate for automotive safety-critical systems and enterprise AI products. Act as the quality gatekeeper for AI-enabled releases, partnering with engineering, product, safety, and OEM stakeholders to identify risks, define release criteria, and ensure production readiness. Collaborate across engineering teams to define validation strategies for emerging AI capabilities, rapidly prototype new evaluation approaches, and standardize successful practices into reusable frameworks. Drive continuous improvement by tracking industry advances in AI evaluation, agentic AI, LLM validation, and RAG systems, translating them into scalable validation capabilities across Sonatus. Qualifications: Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field required (MS preferred). 10+ years of experience in software or systems engineering—including embedded, cloud, networking, security, or automotive domains—with 3+ years leading high-performing engineering or QA organizations. Hands-on experience developing, deploying, testing, or operating AI/ML systems, with strong expertise in modern ML workflows, neural networks, and MLOps. Deep understanding of LLMs, RAG architectures, vector databases, embeddings, retrieval optimization, and agentic AI frameworks such as LangGraph or equivalent orchestration platforms. Proven experience designing and implementing scalable evaluation frameworks for AI systems, including multi-step agentic workflows, regression testing, benchmarking, and automated quality scoring. Strong expertise with hybrid evaluation methodologies combining deterministic validation (citation grounding, structural validation, exact matching) and probabilistic LLM-as-a-Judge techniques (faithfulness, answer relevance, context precision, task completion). Practical experience with RAG evaluation frameworks such as RAGAS, including evaluation tuning, embedding optimization, retrieval quality improvement, and production-scale LLM evaluation pipelines. Experience validating hallucination, grounding, citation accuracy, bias, fairness, toxicity, and factual consistency in production LLM applications. Experience designing systems that verify external knowledge claims and ensure responses are grounded in traceable citations and trusted data sources. Strong experience testing cloud-native platforms and cloud-managed embedded products, including end-to-end system validation. Experience establishing AI governance, safety, compliance, and Responsible AI practices for enterprise or safety-critical systems. Proficiency in Python, Linux, shell scripting, modern test frameworks (PyTest, Playwright, Behave), and engineering productivity tools such as Jenkins and JIRA. Ways to Stand Out: Experience validating AI or agentic systems in safety-critical or regulated industries (automotive, aerospace, medical). Track record building and scaling an AI test/evaluation platform or developer experience used by multiple teams (frameworks, reusable components, reference implementations). Demonstrated wins moving AI testing practices from ad hoc to standardized, organization-wide adoption, with measurable impact on cycle time, quality, or reliability. Experience implementing enterprise-grade AI governance (auditability, monitoring, policy enforcement) in production systems. Deep experience evaluating LLM and RAG systems at scale, including agentic workflows, RAGAS-based evaluation, citation verification, hallucination detection, groundedness, faithfulness, answer relevance, tool/task correctness, and automated regression testing across offline and online feedback loops. Sunnyvale HQ Benefits & Perks Offered: Health care plan (Medical, Dental & Vision) Flexible and Dependent Care Expense program Retirement plan (401k) Life Insurance (Basic, Voluntary & AD&D) Unlimited paid time off per year, 14+ paid holidays Hybrid office work arrangement Complimentary lunches, snacks, and beverages during on-site working days Wellness benefit allowance Phone & Internet reimbursement Computer Accessory Allowance The posted salary range is a general guideline and represents a good faith estimate of what Sonatus ("Company") could reasonably expect to pay for a base salary for this position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, geographic location and external market pay for comparable jobs. The Company reserves the right to modify this range in the future, as needed, as market conditions change. Base Salary Pay Range $240,000 — $290,000 USD
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.