# Software Engineer, Model Lifecycle and Signals
**Company:** [Cursor](https://scaleengineer.com/companies/cursor)
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.
**Role:** Software Engineer
**Seniority:** Senior
**Locations:** New York
**Salary:** 185000–280000 USD
[Apply](https://jobs.ashbyhq.com/cursor/a577902e-28e9-4e68-9ee9-91e34d1b59c4)
Canonical: https://scaleengineer.com/jobs/cursor/software-engineer-model-lifecycle-and-signals
---
## 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.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Foundation in computer science, computer engineering, or related technical discipline providing strong theoretical and practical programming fundamentals."}
- {"name":"Self-Directed Advanced Technical Learning","description":"Demonstrated commitment to continuous learning in AI/ML, distributed systems, or software architecture through certifications, open-source contributions, publications, or technical community involvement."}

### technical

- {"name":"Production Systems Design and Operation","description":"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."}
- {"name":"Evaluation and Measurement Systems","description":"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."}
- {"name":"Data Pipeline Development","description":"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."}
- {"name":"API Design and Backend Architecture","description":"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."}
- {"name":"Proficiency with Machine Learning Deployment","description":"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."}

### experience

- {"name":"Senior Software Engineering Track Record","description":"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."}
- {"name":"Expertise with AI/ML Systems in Production","description":"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."}
- {"name":"Data Systems and Analytics Platform Experience","description":"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

- {"name":"Production Backend Development","description":"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."}
- {"name":"Distributed Systems Design","description":"Strong foundation in designing systems that operate reliably at scale, including load balancing, caching strategies, database optimization, and fault tolerance patterns."}
- {"name":"Data Analysis and SQL","description":"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."}
- {"name":"Model Evaluation and Benchmarking","description":"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."}
- {"name":"Infrastructure and DevOps Practices","description":"Comfort with containerization, orchestration, CI/CD pipelines, and infrastructure-as-code. Experience monitoring production systems, implementing observability, and responding to production incidents."}

### preferred

- {"name":"Large Language Model Expertise","description":"Active 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."}
- {"name":"AI Agent Architecture Experience","description":"Prior exposure to agentic systems, retrieval-augmented generation (RAG), tool use patterns, or prompt engineering at scale. Understanding of emerging agent paradigms and evaluation challenges."}
- {"name":"Ranking and Relevance Systems","description":"Background building ranking algorithms, relevance systems, or recommendation engines. Experience with ranking optimizations, learning-to-rank approaches, or multi-objective optimization at scale."}
- {"name":"Experimentation Platforms","description":"Experience building A/B testing infrastructure, experiment analysis tools, or statistical frameworks for product decisions. Familiarity with multi-armed bandit algorithms or causal inference techniques."}
- {"name":"Open Source ML/Data Infrastructure","description":"Contributions 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

### tools

- {"name":"Containerization and Orchestration","description":"Docker for packaging models and services; Kubernetes for orchestrating deployments at scale. Essential for managing complex inference and routing infrastructure."}
- {"name":"Monitoring and Observability","description":"Experience with Prometheus, Grafana, DataDog, or equivalent tools for building comprehensive monitoring dashboards and alerting systems for production models and services."}
- {"name":"CI/CD and Version Control","description":"Git workflows, CI/CD platforms like GitHub Actions or Jenkins for automated testing and deployment pipelines. Infrastructure-as-code tools like Terraform or similar."}
- {"name":"Experimentation Tools","description":"Familiarity with Statsig, LaunchDarkly, or custom A/B testing platforms for managing feature flags and running controlled experiments on model routing and selection."}

### others

- {"name":"Production Observability","description":"Strong grasp of distributed tracing, logging aggregation, and metrics collection. Ability to instrument systems comprehensively and build dashboards enabling rapid diagnosis of production issues."}
- {"name":"Scalable Architecture Patterns","description":"Deep understanding of caching strategies, database optimization, load balancing, rate limiting, and other architectural patterns required for systems serving millions of requests reliably."}
- {"name":"Data-Driven Decision Making","description":"Comfort with statistical thinking, experimental design, and translating business questions into measurable, testable hypotheses. Ability to validate assumptions through data analysis."}

### databases

- {"name":"PostgreSQL or MySQL","description":"Relational databases for storing model metadata, evaluation results, routing policies, and production telemetry essential for operational decision-making."}
- {"name":"Data Warehousing Solutions","description":"Experience with BigQuery, Snowflake, Redshift, or DuckDB for analyzing large volumes of model performance data and product analytics at scale."}
- {"name":"Real-time Databases","description":"Familiarity with Redis, Apache Kafka, or similar technologies for streaming metrics, real-time feature computation, and low-latency decision-making in routing systems."}

### languages

- {"name":"Python","description":"Primary language for data pipeline development, model evaluation scripts, and backend services integrating with ML systems. Essential for working with model serving and data processing frameworks."}
- {"name":"Go or Rust","description":"Recommended for high-performance backend services handling millions of requests. Go preferred for cloud infrastructure, Rust for performance-critical routing logic."}
- {"name":"SQL","description":"Core competency for querying production databases, analyzing model performance metrics, and extracting insights from telemetry data at scale."}

### frameworks

- {"name":"Model Serving Frameworks","description":"Experience with vLLM, TensorFlow Serving, TorchServe, or similar frameworks for deploying and managing large language models in production environments."}
- {"name":"API Frameworks","description":"Proficiency with FastAPI, Flask, or Django for Python; or equivalent Golang/Rust frameworks for building robust, scalable APIs that route requests to models efficiently."}
- {"name":"Evaluation Frameworks","description":"Familiarity with evaluation tools like HELM, Hugging Face Evaluate, LMOps frameworks, or custom evaluation pipelines for assessing model quality and capabilities."}

## Benefits

### benefits

- {"name":"Equity Compensation","description":"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."}
- {"name":"Comprehensive Health and Wellness","description":"Full medical, dental, and vision coverage. Wellness programs, mental health support, and fitness benefits supporting employee wellbeing and work-life balance."}
- {"name":"Professional Development","description":"Learning budget for conferences, courses, and certifications. Access to technical training programs and opportunities to work with cutting-edge AI models and infrastructure."}
- {"name":"Flexible Work Arrangements","description":"Collaborative office environment in San Francisco with flexibility for remote collaboration. Access to state-of-the-art computing resources for AI research and development."}
- {"name":"Unlimited PTO and Parental Leave","description":"Generous time-off policies supporting work-life balance. Comprehensive parental leave programs for all employees regardless of family structure."}
- {"name":"Impact at Scale","description":"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."}

## Compensation

- **max:** 280000
- **min:** 185000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

- {"name":"Initial Screening and Technical Discussion","description":"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."}
- {"name":"Technical Assessments (2-3 Short Technicals)","description":"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."}
- {"name":"Model Evaluation Deep Dive","description":"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."}
- {"name":"On-Site Project and Team Discussions","description":"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."}
- {"name":"Leadership and Cross-functional Collaboration","description":"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 description
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](https://cursor.com/docs/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