• Define architecture and long-range technical strategy for ML systems and enabling platforms (training/inference orchestration, feature foundations, serving patterns, interoperability layers) across the measurement ecosystem • Lead development and productionization of next-generation ML approaches for identity resolution, audience modeling, and cross-platform measurement; translate advanced algorithms into scalable, production-quality implementations • Guide major data engineering and platform investments required for ML: distributed processing patterns, data routing/storage strategies, data integrity controls, and automation across internal/external data sources • Establish enterprise-grade MLOps, governance, and assurance processes: CI/CD standards, automated evaluation, model versioning/registry practices, drift detection, monitoring, and operational excellence • Lead cross-organization technical decision-making: align stakeholders, define success metrics, and drive complex trade-offs to deliver durable, scalable ML solutions • Coach and develop senior technical talent: mentor Staff/Senior engineers, set engineering standards, and build communities of practice • Champion privacy, security, and responsible AI: ensure privacy-by-design, PII safeguards, and audit readiness (GDPR/CCPA) for ML and data workflows

Role Description Plugging into an LLM API is not the same as building an AI system that works in production under real-world pressure. The gap between those two keeps widening, and the engineers who can bridge it are becoming the most valuable people on any team. These are the people who understand model behaviour, evaluate rigorously, and design systems where AI is a load-bearing component. We're not looking for engineers who have just discovered GenAI. We're looking for engineers whose craft is AI: who reason about model behaviour the way strong software engineers reason about distributed systems, who build evaluations before they build features, and who can tell you exactly why a model is failing and how to fix it. This role is for the engineers our clients rely on when the stakes are highest: when the AI has to actually work, not just demo well. Concretely, you will: - Design and build production AI systems where model behaviour is load-bearing: retrieval architectures, agentic workflows, multi-model orchestration, structured generation pipelines. - Own the evaluation methodology end-to-end. Define what "good" means for a given system, build regression suites, run offline and online experiments, and track drift and degradation once it's in production. - Make model and architecture decisions with real trade-offs in mind (latency, cost, accuracy, data sensitivity, operational complexity). - Debug AI systems at the model level: prompt behaviour, retrieval failure modes, tokenisation edge cases, and generation drift. - Contribute to or drive LLMOps infrastructure: inference serving, caching, monitoring, experimentation, and cost control. - Translate ambiguous business problems into concrete AI system designs, with clear reasoning about what's feasible today versus what's still speculative. - Stay at the frontier of AI/ML research: read papers, evaluate techniques critically, and bring what's genuinely useful into practice. - Use AI coding tools (Claude Code, Cursor, Copilot, or similar) as a natural part of your workflow. Qualifications - 3+ years as a software engineer and/or at least 2+ years of substantial production work on AI/ML or GenAI systems. - Deep understanding of modern AI systems: LLMs, embeddings, vector search, retrieval pipelines, agentic architectures, evaluation frameworks, and production observability for AI. - Working knowledge of classical ML fundamentals: supervised learning, evaluation metrics, overfitting, data distribution shifts. - Strong engineering fundamentals in Python (primary), ideally with working comfort in TypeScript/Node.js. - Proven ability to design and own evaluation methodology, not just use existing frameworks. - Can reason about model behaviour at depth: why a system failed, how to measure improvement, how to build systems that degrade gracefully. - Comfortable at the boundary between research and production. - High agency and technical leadership. - Pragmatic about when AI is the right tool, and when it isn't. - Low ego, high standards. Requirements - Hands-on experience with fine-tuning, LoRA/QLoRA, DPO, or similar adaptation techniques. - Has built evaluation harnesses from scratch: LLM-as-judge pipelines, regression suites, red-teaming setups, or human-in-the-loop eval workflows. - Experience deploying AI systems under serious latency or cost constraints. - Contributions to or deep familiarity with the open-source ML ecosystem. - Experience with the full LLMOps lifecycle: model routing, inference serving, caching, monitoring for drift, cost attribution. - Can discuss specific papers with informed opinions. - Has worked on classical ML problems and can speak to what changes with GenAI and what doesn't. - Active contributor to the AI tooling or research community. - Has shipped real products using AI-assisted coding workflows. Benefits - Problems worth your depth: work on AI systems that are load-bearing, not decorative. - Access to frontier AI as part of your daily work: latest models, open-source weights, and tooling available without an approval gauntlet. - A team that engages at depth: peers to push back on you, and engineers to mentor. - Agile Company Culture and the Best Team. - Global Projects & Opportunities. - Social Events & Team Building. - Continuous Development: Training & Development, Growth Opportunities. - Flexible Working: Remote Friendly Culture. - Great Equipment & Tools. - Flexible Benefits. - Extra Days Off. - Health Insurance. Hiring Process Our process is direct and designed to respect your time: - PX interview - Technical interview - Final conversation - Offer The technical interview focuses on real problems, not algorithmic puzzles. We evaluate candidates holistically. If you don't meet every requirement listed above, apply anyway. We care more about how you think and work than whether your CV matches a checklist. See you on the other side!

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company’s three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles. Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront. We’re looking for a Machine Learning Engineer to join Snap Inc! What you’ll do: - Build and deploy machine learning models that power core products, serving millions of Snapchatters - Apply modern ML techniques to solve large-scale, real-world problems - Own the full ML lifecycle from data analysis to production deployment - Partner with cross-functional teams to prototype and launch ML-driven features Knowledge, Skills & Abilities: - Strong understanding of machine learning approaches and algorithms - Able to prioritize duties and work well on your own - Ability to work with both internal and external partners - Skilled at solving open ambiguous problems - Strong collaboration and mentorship skills Minimum Qualifications: - Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience - 5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience - Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning Preferred Qualifications: - Advanced degree in computer science or related field - Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks - Experience working with machine learning, ranking infrastructures, and system design If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information. "Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable). Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success! Compensation In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future. Zone A (CA, WA, NYC): The base salary range for this position is $209,000-$313,000 annually. Zone B: The base salary range for this position is $199,000-$297,000 annually. Zone C: The base salary range for this position is $178,000-$266,000 annually. This position is eligible for equity in the form of RSUs.

• Take ownership of machine learning problems from concept to production • Design, build, and deploy predictive models (e.g. pricing, bidding, optimization, forecasting) • Develop scalable feature engineering and data pipelines for large-scale datasets • Define experimentation frameworks (A/B testing, offline validation, model comparison) • Ensure production-grade MLOps: monitoring, retraining, drift detection, reliability • Collaborate closely with DevOps, Product, Engineering teams to align ML with business impact • Quantify model impact on revenue, margin, and performance KPIs • Contribute to building our long-term ML architecture and best practices