[All remote jobs](https://jobicy.com/jobs.md)[![SeatGeek logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/72d8f3a6-221.jpeg)](https://jobicy.com/company/seatgeek.md)[SeatGeek](https://jobicy.com/company/seatgeek.md) # Senior Machine Learning Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/seatgeek.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 145k–209k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished20 Oct 2026Apply before19 Nov 2026Listing views60Application actions3Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. SeatGeek is seeking a senior machine learning engineer to productionize scalable ML systems for ticket discovery, pricing, personalization, demand forecasting, and fraud prevention. The role combines software engineering, MLOps, and applied machine learning, with responsibility for model-serving infrastructure, feature stores, real-time inference, and automated deployment pipelines. The engineer will work embedded with product teams and partner closely with data scientists, product managers, and software engineers. Candidates need strong Python experience, production ML expertise, cloud and containerization knowledge, and a product-focused approach to reliable systems. The position supports fully remote work for candidates in the United States. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior-level role requiring both deep ML engineering capability and strong production software design skills. Building reliable real-time ML infrastructure for pricing, personalization, and fraud systems creates significant scale, reliability, and business-impact complexity. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$177,000US market range (per year)$165k–$220k0$242.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base salary range of $145,000–$209,000 USD. The annual cadence is inferred from the US senior full-time role context and the compensation scale; the listing does not explicitly attach a pay period to the base-salary range. The midpoint is $177,000 per year, with an estimated US market range of $165,000–$220,000 annually for comparable senior ML engineering roles. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Model Serving](https://jobicy.com/jobs?search_keywords=Model%20Serving.md)[Feature Stores](https://jobicy.com/jobs?search_keywords=Feature%20Stores.md)[Real-Time Inference](https://jobicy.com/jobs?search_keywords=Real-Time%20Inference.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[SageMaker](https://jobicy.com/jobs?search_keywords=SageMaker.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[Containerization](https://jobicy.com/jobs?search_keywords=Containerization.md)[Airflow](https://jobicy.com/jobs?search_keywords=Airflow.md)[TensorFlow](https://jobicy.com/jobs?search_keywords=TensorFlow.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[A/B Testing](https://jobicy.com/jobs?search_keywords=AB%20Testing.md)[ML Monitoring](https://jobicy.com/jobs?search_keywords=ML%20Monitoring.md) Sample interview questionsDescribe a machine learning system you deployed to production and how you measured its impact.I would explain the model’s business objective, data flow, serving architecture, deployment process, reliability controls, and measurable outcomes. I would include both ML metrics, such as precision or calibration, and product metrics, such as conversion, revenue, latency, or fraud-loss reduction. How would you design a real-time model-serving platform for pricing or personalization? I would separate offline training and feature generation from low-latency online inference, define feature freshness requirements, and use versioned models with controlled rollout mechanisms. I would also implement observability for latency, errors, data drift, prediction quality, and business outcomes, with safe fallback behavior if dependencies fail. What practices do you use to make ML pipelines reliable and reproducible? I use versioned code, data, features, model artifacts, and configuration; automated validation gates; reproducible training environments; and CI/CD for deployment. I also establish lineage, monitoring, alerting, rollback procedures, and clear ownership for pipeline failures. How do you decide whether an experimental model is ready for production? I assess offline performance, robustness across relevant segments, serving cost and latency, data availability, operational risk, and expected business value. I then use a staged rollout, such as shadow testing or an A/B test, with predefined success criteria and rollback thresholds. How do you collaborate with data scientists and product teams when converting a prototype into a production system? I align early on the product objective, success metrics, data contracts, inference requirements, and operational constraints. I help translate research assumptions into maintainable interfaces and deployment plans, while ensuring stakeholders understand model limitations, monitoring needs, and the trade-offs between accuracy, latency, cost, and user experience. Opportunity details ## About this role. ### SeatGeek believes live events are powerful experiences that unite humans. With our technological savvy and fan-first attitude we’re simplifying and modernizing the ticketing industry. SeatGeek is a technology innovator on a mission to disrupt the $300 billion ticketing industry. We have the product, vision, and team to make life better for performers, venues, and fans, and build a generational consumer brand in the process. All we’re missing is you. You will join a group that bridges the gap between research and production-ready ML systems. Your work will directly impact how millions of fans discover and purchase tickets, how we optimize pricing and inventory, how we personalize the SeatGeek experience, and how we prevent fraud across our marketplace. You will design and build ML infrastructure and services that operate at scale, turning complex algorithms into reliable, fast, and maintainable systems that drive business value. ### What you’ll do * Design, build, and deploy machine learning models and systems that operate reliably at scale in production * Build and maintain ML infrastructure including feature stores, model serving platforms, and real-time inference pipelines * Embed on a product engineering team and collaborate closely with data scientists, PMs ,and Software Engineers to translate research and experimental models into production-ready systems * Solve complex technical challenges unique to the ticketing industry, including real-time pricing optimization, demand forecasting, and fraud detection * Develop automated ML pipelines for training, validation, deployment, and monitoring using MLOps best practices * Work across team and discipline boundaries to evangelize ML capabilities and build them into SeatGeek’s core product offerings ### What you have * Experience building and deploying machine learning systems in production environments. We’ll be interested in hearing about the systems you’ve built, the scale you’ve operated at, and the business impact you’ve driven * 4+ years of experience in software engineering with at least 2+ years focused on machine learning systems and MLOps * Strong programming skills in Python and experience with ML frameworks like scikit-learn, TensorFlow, PyTorch, or similar * Experience with cloud platforms and containerization technologies * Understanding of both batch and real-time ML systems, including experience with model serving, A/B testing, and performance monitoring * Passion for software craftsmanship and product. You have well-considered opinions about how systems should be built, and hold yourself and your code to a high standard * A product mindset. You think beyond the model accuracy, about user experience, business impact, system reliability, and what makes a great product tick * Commitment to your teammates. You enjoy working with a diverse group of people with different experiences and take pride in mentoring and learning from others ### Our stack You do not need experience with all of these, but we thought you might be curious. What we care about is your experience, skills, and approach to problem solving. Tools can be learned. * Languages + Frameworks: Python + FastAPI, Go, C# + .NET Core * Datastores: Postgres, MemcachedRedis, Elasticsearch * Cloud: AWS (SageMaker, Redshift, ECS), Airflow for orchestration * Version control: Gitlab * AI Tooling: Cursor, Github Copliot, Claude Code * Observability: Datadog ### Perks * Equity stake * Discretionary annual bonus * Flexible work environment, allowing you to work as many days a week in the office as you’d like or 100% remotely * A WFH stipend to support your home office setup * Unlimited PTO * Up to 16 weeks of fully-paid family leave * 401(k) matching * Student loan matching program * Health, vision, dental, and life insurance * Up to $25k towards family building, reproductive health services and Gender-affirming care * $500 per year for wellness expenses * Subscriptions to Headspace (meditation), Headspace Care (therapy), and One Medical * $360 per quarter to spend on tickets to live events * Annual subscription to Spotify, Apple Music, or Amazon music The salary range for this role is $145,000 – $209,000 USD. This role is equity eligible. In addition, you may receive a discretionary annual bonus based on individual and company performance. Actual compensation packages within that range are based on a wide array of factors unique to each candidate, including but not limited to skill set, years and depth of experience, certifications, and specific location. SeatGeek is committed to providing equal employment opportunities to all employees and applicants for employment regardless of race, color, religion, creed, age, national origin or ancestry, ethnicity, sex, sexual orientation, gender identity or expression, disability, military or veteran status, or any other category protected by federal, state, or local law. As an equal opportunities employer, we recognize that diversity is a positive attribute and we welcome the differences and benefits that a diverse culture brings. Come join us! To review our candidate privacy notice, [click here](https://seatgeek.com/privacy/USApplicantNotice). ### #LI-Remote Show more [Apply now >](https://jobicy.com/jobs/156066-senior-machine-learning-engineer-3.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. 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