Software Engineer, Model Lifecycle and Signals
Software Engineer · Senior · Full Time
Opens Cursor's application page
Role
What you'll do.
Join Cursor's Model Lifecycle and Signals team as a Software Engineer to own the critical bridge between frontier AI models and user-facing products at unprecedented scale. This role combines infrastructure, data engineering, and product expertise to evaluate, deploy, and optimize model performance across a rapidly growing AI platform used globally. You'll architect evaluation systems, build intelligent model routers, and instrument production telemetry that directly shapes the next generation of AI models and products.
Responsibilities
- Model Deployment and Lifecycle Management: Architect and operate systems that bring frontier models from preview to production, manage model versioning and seamless deprecation, and ensure zero-downtime transitions for millions of users across Cursor's product suite. Own end-to-end model rollout orchestration including canary deployments, A/B testing infrastructure, and rollback procedures.
- Build Intelligent Model Routing Infrastructure: Design and implement the Cursor Router—a sophisticated load balancing and model selection system that dynamically routes requests to optimal models based on quality, latency, and cost considerations at massive scale. Optimize routing decisions using real-time performance metrics, user context, and model capabilities while maintaining strict SLA requirements.
- Develop Comprehensive Evaluation and Measurement Systems: Build automated evaluation pipelines and benchmarking frameworks to assess internal and external model candidates before production launch. Create measurement systems that translate ambiguous model quality concepts into concrete, actionable metrics and provide meaningful feedback to model development teams.
- Instrument Production Monitoring and Signals: Create observability infrastructure that captures model behavior signals in production, identifying where models excel and where they degrade. Build dashboards and alerting systems that surface model performance anomalies, user satisfaction metrics, and opportunities for model optimization across the full product experience.
- Build Experimentation and Analysis Platforms: Architect experimentation infrastructure enabling A/B tests of new models and features at scale. Develop statistical analysis tools and pipelines that extract insights from production data, enabling data-driven decisions for model selection, reranking strategies, and product improvements.
- Cross-functional Technical Collaboration: Partner closely with research teams on model evaluation requirements, work with inference engineers on deployment optimization, collaborate with product managers on user-centric metrics, and support data scientists with infrastructure for model analysis. Be the technical bridge translating between research, infrastructure, and product domains.
Qualifications
What we look for.
Technical
Production Systems Design and Operation
Proven ability to architect, deploy, and operate large-scale production systems serving millions of requests. Experience building systems with high availability requirements, robust error handling, monitoring, and graceful degradation strategies.
Evaluation and Measurement Systems
Hands-on experience building evaluation frameworks, benchmarking pipelines, or model serving systems. Demonstrated expertise translating qualitative requirements into quantitative metrics and building automated assessment infrastructure.
Data Pipeline Development
Strong capability in building scalable data pipelines for ingestion, transformation, and analysis. Experience with distributed data processing, stream processing, or event-driven architectures that handle high-volume telemetry.
API Design and Backend Architecture
Deep experience designing RESTful or gRPC APIs, building efficient backend services, and optimizing for performance under load. Comfortable with service-oriented architecture patterns and designing systems for scalability.
Proficiency with Machine Learning Deployment
Working knowledge of ML model deployment considerations including inference optimization, model versioning, serving patterns, and integration with production systems. Understanding of model serving frameworks and containerization technologies.
Education
Bachelor's Degree in Computer Science or Related Field
Foundation in computer science, computer engineering, or related technical discipline providing strong theoretical and practical programming fundamentals.
Self-Directed Advanced Technical Learning
Demonstrated commitment to continuous learning in AI/ML, distributed systems, or software architecture through certifications, open-source contributions, publications, or technical community involvement.
Experience
Senior Software Engineering Track Record
Minimum 5-7 years of professional software engineering experience with demonstrated ability to own complex problems end-to-end. Track record of shipping production systems that scale and reliable infrastructure that others depend on daily.
Expertise with AI/ML Systems in Production
Substantial experience building, evaluating, or operating AI/ML systems at production scale. Prior work with model evaluation frameworks, experimentation platforms, ranking systems, or AI infrastructure that handles millions of inferences.
Data Systems and Analytics Platform Experience
Background building data infrastructure, analytics platforms, or real-time metrics systems. Experience instrumenting systems for observability and translating data into actionable insights for product and engineering teams.
Skills
Required
Production Backend Development
Advanced proficiency in building scalable backend services, APIs, and infrastructure. Experience with Go, Rust, Python, or Java in production environments serving millions of requests with high reliability requirements.
Distributed Systems Design
Strong foundation in designing systems that operate reliably at scale, including load balancing, caching strategies, database optimization, and fault tolerance patterns.
Data Analysis and SQL
Expert-level SQL skills for complex data analysis and query optimization. Ability to work with large datasets, write efficient queries, and extract insights from production telemetry.
Model Evaluation and Benchmarking
Hands-on experience designing evaluation methodologies, building benchmark suites, and defining metrics for model assessment. Ability to think critically about measurement design and experimental rigor.
Infrastructure and DevOps Practices
Comfort with containerization, orchestration, CI/CD pipelines, and infrastructure-as-code. Experience monitoring production systems, implementing observability, and responding to production incidents.
Preferred
Large Language Model Expertise
Nice to haveActive experience working with and evaluating LLMs like GPT-4, Claude, or Grok. Hands-on familiarity with model capabilities, limitations, and real-world behaviors across different application domains.
AI Agent Architecture Experience
Nice to havePrior exposure to agentic systems, retrieval-augmented generation (RAG), tool use patterns, or prompt engineering at scale. Understanding of emerging agent paradigms and evaluation challenges.
Ranking and Relevance Systems
Nice to haveBackground building ranking algorithms, relevance systems, or recommendation engines. Experience with ranking optimizations, learning-to-rank approaches, or multi-objective optimization at scale.
Experimentation Platforms
Nice to haveExperience building A/B testing infrastructure, experiment analysis tools, or statistical frameworks for product decisions. Familiarity with multi-armed bandit algorithms or causal inference techniques.
Open Source ML/Data Infrastructure
Nice to haveContributions to or deep familiarity with open-source tools like Ray, Weights & Biases, MLflow, DuckDB, or similar technologies for machine learning operations and data management.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 185,000 – 280,000
Equity·Stock options
Benefits
Equity Compensation
Meaningful equity stake in Cursor as a rapidly growing AI company backed by leading venture capital. Opportunity for significant upside as the company scales its AI products globally.
Comprehensive Health and Wellness
Full medical, dental, and vision coverage. Wellness programs, mental health support, and fitness benefits supporting employee wellbeing and work-life balance.
Professional Development
Learning budget for conferences, courses, and certifications. Access to technical training programs and opportunities to work with cutting-edge AI models and infrastructure.
Flexible Work Arrangements
Collaborative office environment in San Francisco with flexibility for remote collaboration. Access to state-of-the-art computing resources for AI research and development.
Unlimited PTO and Parental Leave
Generous time-off policies supporting work-life balance. Comprehensive parental leave programs for all employees regardless of family structure.
Impact at Scale
Direct influence on AI products used by millions globally. Your infrastructure and evaluation systems directly shape the quality of AI experiences for Cursor's user base.
Process
Interview steps.
- 01
Initial Screening and Technical Discussion
Conversation with the hiring team about your background in systems engineering, AI/ML infrastructure, and past experience with production ML deployment. They'll assess your understanding of the domain and curiosity about frontier models.
- 02
Technical Assessments (2-3 Short Technicals)
Focused technical interviews covering system design for model routing, data pipeline architecture, evaluation framework design, and problem-solving in ML infrastructure scenarios. Expect discussions on real-world tradeoffs between model quality, latency, and cost.
- 03
Model Evaluation Deep Dive
Technical conversation exploring your approach to building evaluation systems, defining model quality metrics, and translating fuzzy requirements into concrete measurements. May include case studies of model assessment challenges.
- 04
On-Site Project and Team Discussions
Full-day on-site at Cursor's San Francisco office where you'll work on a hands-on project related to model routing, evaluation, or signals infrastructure. You'll collaborate with team members, present your approach, and discuss ideas with research, product, and engineering stakeholders.
- 05
Leadership and Cross-functional Collaboration
Conversations with senior engineers and team leads assessing your ability to collaborate across research, infrastructure, and product teams. Discussion of how you approach ambiguous problems and communicate technical insights to non-engineers.
Full posting
Original listing.
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
About the Role
As a Software Engineer on the Model Lifecycle and Signals team at SpaceXAI, you'll own the bridge between frontier models and every user facingCursor product, used at a scale few teams ever see. You'll decide when we use which model, spot where models go off track and build the guardrails to keep them on it, and shape what the next generation of models should learn.
This role sits at the intersection of infrastructure, data, and product. You'll bring models from preview to production, dig into real-world data to find out what's actually working, and partner with research, inference, product, and data science teams to turn that evidence into better products and better models.
Every improvement you make compounds across our products and every model we ship.
What you'll work on
Get new frontier models into users' hands the day they're ready, and retire old ones without anyone noticing.
Evaluate internal and external preview models before launch and give meaningful feedback to the teams building them.
Build Cursor Router: decide which model handles each request, balancing quality, speed, and cost at massive scale.
Build the eyes and ears of the product, and the experimentation platform that puts them to work: signals that show where each model shines, where it goes off track, and what 'better' actually means.
You may be a fit if
You're an exceptional engineer with a track record of shipping production systems people rely on. You own problems end to end, and you'd rather automate a painful process than repeat it.
You've built and operated evaluation, measurement, or model serving systems, such as AI evals, experimentation, ranking/relevance, or routers. You can turn ambiguous "quality" questions into concrete metrics, pipelines, and decisions.
You're fluent with data and can collaborate effectively with data scientists and researchers.
You have strong taste and real curiosity about model and agent behavior. You try new models the day they come out and have opinions about where each one shines.
Applying
If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.
#LI-DNI
Redirects to Cursor's application page.
Other roles
More at Cursor.
Field Engineer, Commercial
Mid
Software Engineer, Compute Infra
Senior
Field Engineer, Commercial
Mid
Software Engineer, Research Tooling
Mid
Software Engineer, RL Data
Senior