Staff Machine Learning Engineer

SurveyMonkey is the world’s most popular platform for surveys and forms, built for business—loved by users. We combine powerful capabilities with intuitive design, effectively serving every use case, from customer experience to employee engagement, market research to payment and registration forms. With built-in research expertise and AI-powered technology, it’s like having a team of expert researchers at your fingertips. Trusted by millions—from startups to Fortune 500 companies—SurveyMonkey helps teams gather insights and information that inspire better decisions, create experiences people love, and drive business growth. Discover how at surveymonkey.com http://surveymonkey.com. What we’re looking for: We're seeking a Staff Machine Learning Engineer to join the Product Data Science team, a key contributor to our AI/ML growth. As a technical leader, you'll join a team building enterprise-scale NLP, ML and AI systems that power the product across the organisation. This role is an opportunity to architect ML/AI solutions from the ground up: crafting reusable and impactful components, defining architectures and models for complex tasks, and serving as a technical expert and mentor who shapes both our technology stack and team capabilities. You'll have the autonomy to define end-to-end solutions for ambiguous, high-impact problems while contributing to strategic direction and building the next generation of ML talent at the company. What you’ll be working on: - Serve as a technical leader for the ML/AI product initiatives. Authoring, approving, and guiding architectural decisions. Establishing standards and frameworks that can be adopted across ML initiatives.

• Handle deliverables, delegate effectively, and mentor engineers across teams.
• Architected scalable ML platforms and production data pipelines, collaborating cross-functionally to integrate new data sources.
• Build and own end-to-end ML/AI solutions within the product, maintaining long-term ownership through different stages of deployment, refinement, and iterative enhancements - Create novel and traditional ML/AI implementations for the in-product experience, collaborating with Product, Design, Front- and Back-End developers to educate teams and iterate through solutions and designs.
• Build end-to-end monitoring and telemetry systems to detect complex failure modes, deterioration of predictive accuracy, and usage patterns.
• Deliver tailored AI solutions by training, fine-tuning, and deploying models ranging from statistical methods to cutting-edge LLMs.
• Build productive partnerships across all stakeholder levels, from non-technical to domain experts, with an open, inclusive, and expert outlook.
• Drive continuous improvement and innovation through research, proposing adoption strategies of external solutions, identifying knowledge gaps within the team, and ensuring hiring and training initiatives address capability needs.

• Strong expertise in Natural Language Processing, statistical modelling and analysis, and modern ML methods. Deep understanding of not just the models, but what's happening within them—hands-on experience building novel architectures for bespoke product solutions.
• Experience handling data at scale with familiarity in Human-In-The-Loop labelling, training and scaling usable data, plus foundational exploratory data analysis.
• Proven expertise in creating evaluations and evaluation platforms for non-deterministic systems, including LLM-as-Judge techniques, inferred signals, and traditional model evaluation mechanisms to validate performance and maximise ML/AI impact.
• Experience developing production monitoring and creating feedback loops using active feedback, passive signals, and user behaviours to identify key performance indicators for user journeys and experiences.
• SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).
• Demonstrated experience building LLM-powered product integrations, including Agents and autonomous processes, RAG and context enrichment, and modern prompt engineering methods with guided generation and oversight.
• Proven leadership and mentorship capabilities as a technical leader with the ability to mentor engineers across teams and manage complex deliverables.

• Highlight any production‑grade NLP or LLM projects in your resume; include metrics like latency, throughput, or scale when possible.
• Prepare a short case study showing how you built a monitoring system for model drift and what actions you triggered.
• Demonstrate mentorship experience: be ready to discuss how you guided junior engineers through complex ML problems.
• Bring a portfolio of code (GitHub, notebooks) that showcases end‑to‑end pipelines, not just research prototypes.
• During interviews, ask about the data labeling workflow and feedback loops—showing curiosity about the product’s data pipeline impresses interviewers.
• If offered, negotiate for a remote‑work stipend or equipment budget to ensure an optimal home office setup.
• Watch for vague answers about production monitoring; a clear roadmap for model observability is essential for this role.