# Machine Learning Engineer, Implicit Signals
**Company:** [OpenAI](https://scaleengineer.com/companies/openai)
Join OpenAI's Implicit Signals team as a Machine Learning Engineer to build and deploy classification and evaluation systems that provide critical feedback to post-training and research teams. This role combines machine learning expertise with production engineering to develop reliable, scalable classifiers that help researchers assess model behavior and drive improvements. You'll own the complete lifecycle of ML systems—from problem formulation and supervised fine-tuning through production deployment, optimization, and continuous improvement—working at the intersection of rigorous scientific evaluation and practical software engineering.
**Role:** ML Engineer
**Seniority:** Senior
**Locations:** San Francisco
**Salary:** 381000–510000 USD
[Apply](https://jobs.ashbyhq.com/openai/a01fc9ed-de08-4326-9206-4ee3f08000dc)
Canonical: https://scaleengineer.com/jobs/openai/machine-learning-engineer-implicit-signals
---
## Responsibilities

- Build and Deploy Production Classification Systems: Develop, deploy, and operate production-grade classification and evaluation systems that deliver reliable, actionable feedback to post-training and research teams. Ensure systems meet accuracy, reliability, and performance standards while maintaining seamless integration with downstream research workflows.
- Optimize Production Inference at Scale: Engineer efficient inference pipelines by optimizing model serving, implementing intelligent batching and caching strategies, and balancing throughput, latency, and compute utilization to minimize operational costs while maintaining quality standards.
- Train and Refine Classifiers: Implement supervised fine-tuning and knowledge distillation techniques to develop high-quality classifiers. Conduct rigorous validation testing, measure performance against quality benchmarks, and optimize for deployment before production release.
- Ensure Production Reliability and Observability: Establish comprehensive monitoring and observability for deployed classifiers, rapidly diagnose production issues, implement fixes, and continuously enhance performance through iterative improvements and performance analysis.
- Design Rigorous Evaluation Frameworks: Architect and implement rigorous evaluation methodologies that assess model accuracy, calibration, generalization across diverse inputs, and potential measurement bias. Guide production tradeoff decisions with empirical evidence and statistical rigor.
- Collaborate with Cross-Functional Teams: Partner closely with software engineers to integrate models into reliable execution systems and manage safe model rollouts. Work with researchers and data scientists to translate research needs into concrete modeling objectives and production requirements.
- Develop Reusable ML Infrastructure Tools: Build and maintain engineering tools and frameworks that accelerate classifier training, evaluation, deployment, and iteration cycles. Create abstractions and utilities that increase team velocity and reduce time-to-production for future classifier development.
- Translate Research Questions into ML Problems: Collaborate with post-training researchers to decompose open-ended questions about model behavior into well-defined, tractable machine learning problems with clear success criteria and evaluation methodologies.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Formal education in computer science, machine learning, statistics, engineering, or closely related discipline providing foundational knowledge for ML systems development."}

### technical

- {"name":"End-to-End ML Project Ownership","description":"Proven experience taking machine learning projects from initial problem formulation and experimental phase through production deployment, including monitoring and optimization post-launch."}
- {"name":"Supervised Learning and Statistical Foundations","description":"Strong theoretical understanding of supervised learning algorithms, statistical inference, hypothesis testing, experimental design, and validation methodologies for robust model evaluation."}
- {"name":"Model Evaluation and Error Analysis","description":"Practical expertise in comprehensive model evaluation including performance metrics, error analysis, systematic bias detection, and strategies for managing imperfect or noisy labels in real-world datasets."}
- {"name":"Production ML Systems Development","description":"Strong software engineering capabilities with demonstrated ability to architect, build, and maintain scalable, maintainable production machine learning systems with robust testing and deployment pipelines."}
- {"name":"Inference Performance Optimization","description":"Deep understanding of inference performance characteristics, ability to reason about and optimize tradeoffs between model quality, inference latency, and computational resource costs."}
- {"name":"Scientific and Pragmatic Problem-Solving","description":"Ability to balance rigorous scientific methodology with practical engineering considerations, making data-driven decisions while maintaining shipping velocity in fast-paced environments."}

### experience

- {"name":"5+ Years ML and Software Engineering Experience","description":"Minimum five years of professional experience in machine learning and software engineering roles with demonstrated growth and increasing responsibility in production ML systems."}
- {"name":"Production ML Systems Deployment","description":"Hands-on experience deploying, operating, and improving machine learning systems in production environments, managing real-world constraints including latency, cost, and reliability requirements."}
- {"name":"Cross-Functional Collaboration","description":"Demonstrated ability to work effectively with research teams and software engineers on complex, ambiguous problems, translating between research objectives and engineering implementations."}

## Skills

### required

- {"name":"Python","description":"Expert-level Python proficiency for implementing machine learning algorithms, data processing, model training pipelines, and production inference systems."}
- {"name":"Machine Learning Frameworks","description":"Proficiency with PyTorch, TensorFlow, or similar frameworks for building, training, fine-tuning, and deploying deep learning models at scale."}
- {"name":"Model Training and Optimization","description":"Expertise in supervised learning, model optimization techniques, hyperparameter tuning, and advanced training strategies like fine-tuning and knowledge distillation."}
- {"name":"Statistical Analysis","description":"Strong foundations in statistical methods, hypothesis testing, confidence intervals, and experimental design for rigorous model evaluation and comparison."}
- {"name":"Production Systems Design","description":"Experience designing and implementing robust systems for model inference, serving, and monitoring in production environments with reliability and scalability requirements."}
- {"name":"Data Pipeline Engineering","description":"Ability to build efficient data processing pipelines, implement feature engineering, data validation, and maintain data quality for ML model training and inference."}
- {"name":"Model Evaluation and Metrics","description":"Expertise in defining appropriate evaluation metrics, conducting rigorous model assessment, analyzing performance across different scenarios, and detecting systematic biases."}
- {"name":"Software Engineering Best Practices","description":"Strong proficiency in software engineering principles including code organization, testing strategies, version control, continuous integration, and maintainable system architecture."}

### preferred

- {"name":"Model Distillation","description":"Experience with knowledge distillation techniques for creating efficient, compact models that maintain performance while reducing inference costs and latency."}
- {"name":"Representation Learning","description":"Familiarity with representation learning and embedding methods for developing rich feature representations that improve model performance and generalization."}
- {"name":"Model Calibration","description":"Experience with probability calibration techniques, ensuring model confidence scores accurately reflect true prediction probabilities for reliable downstream decision-making."}
- {"name":"Efficient Inference Optimization","description":"Experience optimizing inference systems for efficiency through quantization, pruning, model compression, batching strategies, and hardware-aware optimization techniques."}
- {"name":"AI Agent Utilization","description":"Proficiency with modern AI agents and large language models to accelerate development velocity across multiple workstreams and improve productivity in fast-paced research environments."}
- {"name":"Classification System Design","description":"Background in building and deploying classification systems for production use, including handling class imbalance, multi-class problems, and real-world labeling challenges."}
- {"name":"Model Serving and Inference Frameworks","description":"Experience with model serving platforms like TensorFlow Serving, TorchServe, or similar infrastructure for deploying and managing models at scale with low latency."}
- {"name":"LLM Fine-tuning","description":"Experience fine-tuning large language models, implementing parameter-efficient methods, and optimizing large model inference for production deployment."}

## Tech stack

### tools

- {"name":"Experiment Tracking and MLOps Tools","description":"Tools like MLflow, Weights & Biases, or similar for experiment management, tracking metrics, and versioning models during development and iteration."}
- {"name":"Model Serving Platforms","description":"Inference serving infrastructure for deploying models, managing versions, handling traffic, and monitoring performance in production environments."}
- {"name":"Monitoring and Observability","description":"Systems for comprehensive monitoring of deployed classifiers including performance metrics, data drift detection, and production issue diagnosis."}
- {"name":"CI/CD Pipelines","description":"Continuous integration and deployment infrastructure for automated testing, model validation, and safe rollout of classification system updates."}

### others

- {"name":"Supervised Fine-tuning","description":"Techniques for adapting pre-trained models through supervised training on task-specific data to create specialized classifiers."}
- {"name":"Knowledge Distillation","description":"Methods for training smaller, efficient student models to mimic larger teacher models while maintaining performance with reduced computational requirements."}
- {"name":"Model Calibration","description":"Approaches for ensuring model confidence scores accurately reflect prediction probability, crucial for reliable downstream decision-making systems."}
- {"name":"Efficient Inference","description":"Optimization strategies including quantization, pruning, and architectural improvements to reduce model size and inference latency while maintaining accuracy."}

### databases

- {"name":"Data Storage and Retrieval Systems","description":"Distributed data systems for storing training datasets, evaluation results, and serving features efficiently to production inference pipelines."}

### languages

- {"name":"Python","description":"Primary language for machine learning development, model training, data processing, and production inference pipeline implementation."}

### frameworks

- {"name":"PyTorch","description":"Modern deep learning framework used for building, training, and deploying neural network classifiers with flexible research and production capabilities."}
- {"name":"TensorFlow","description":"Comprehensive machine learning platform supporting model development, training optimization, and production deployment at scale."}

## Benefits

### benefits

- {"name":"Competitive Compensation Package","description":"Comprehensive compensation reflecting market rates for senior machine learning engineers in the Bay Area, including salary commensurate with experience and impact."}
- {"name":"Equity Participation","description":"Significant equity grants providing direct ownership stake in OpenAI's success and long-term value creation as the company shapes the future of AI."}
- {"name":"Health and Wellness Coverage","description":"Comprehensive medical, dental, and vision insurance options, mental health support, and wellness programs to support overall health and well-being."}
- {"name":"Unlimited PTO","description":"Flexible time off policy enabling work-life balance while maintaining the flexibility needed to manage individual workload and personal priorities."}
- {"name":"Professional Development","description":"Opportunities to work on cutting-edge AI research and machine learning challenges, learning from world-class researchers and engineers at the frontier of AI."}
- {"name":"Collaborative Research Environment","description":"Access to state-of-the-art compute resources, infrastructure, and tools for developing and deploying machine learning systems, plus collaboration with leading AI researchers."}
- {"name":"Career Growth Opportunities","description":"Clear pathways for advancement in a rapidly growing organization, with opportunities to lead initiatives, mentor team members, and drive technical direction."}
- {"name":"Meaningful Impact","description":"Direct influence on OpenAI's mission to develop safe, beneficial AI systems that benefit humanity, with work that shapes the evaluation and improvement of frontier AI models."}

## Compensation

- **max:** 450000
- **min:** 180000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

- {"name":"Initial Screening and Application Review","description":"Review of your resume, background, and relevant experience in machine learning and production systems. Hiring team assesses alignment with core technical requirements and project experience."}
- {"name":"Phone Screening with Hiring Manager","description":"Conversation with the hiring manager covering your background, specific experiences taking ML projects to production, approach to problem-solving, and questions about the role and team."}
- {"name":"Technical Depth Interview","description":"In-depth technical discussion evaluating machine learning foundations, supervised learning concepts, statistical knowledge, model evaluation methodology, and experience optimizing production systems."}
- {"name":"ML Systems Design Interview","description":"Design-focused interview where you discuss approaches to building production ML systems, inference optimization strategies, tradeoffs between model quality and computational cost, and system architecture decisions."}
- {"name":"Code Implementation or Problem-Solving Session","description":"Technical exercise demonstrating practical coding ability, algorithm implementation, or problem-solving approach relevant to machine learning and systems engineering challenges."}
- {"name":"Research Collaboration Interview","description":"Conversation with a research team member assessing your ability to translate research needs into ML problems, work on ambiguous challenges, and communicate technical concepts effectively with researchers."}
- {"name":"Software Engineering Practices Interview","description":"Discussion of software engineering approach, experience building maintainable systems, testing strategies, deployment practices, and philosophy on balancing scientific rigor with shipping velocity."}
- {"name":"Leadership and Team Fit Conversation","description":"Final conversation with senior leadership or team leads evaluating cultural fit, collaboration style, communication clarity, and your vision for the role and impact at OpenAI."}

## Full description
### **About the Team**

Our team builds classification and evaluation systems that give post-training and research teams fast, reliable feedback on model behavior. We combine machine learning and production engineering to make these capabilities accurate, efficient, and useful at scale, helping researchers assess changes and improve models and products.

We work closely with research teams to develop useful measurements and make them practical to run. Our work spans classifier development, rigorous evaluation, and efficient production inference, with ownership from initial experimentation through deployment and continued improvement.

### **About the Role**

We’re looking for a machine learning engineer to develop new classification and evaluation capabilities and bring them into production. You’ll help turn open-ended questions about model behavior into well-defined ML problems, build methods that address them, and establish how reliably those methods work.

This role combines traditional machine learning, model evaluation, and real-time engineering. You’ll own classifiers throughout their lifecycle, including supervised fine-tuning, distillation, evaluation, deployment, and production inference. You’ll work closely with researchers to identify valuable problems and with software engineers to make promising approaches useful at scale.

### **In this role, you will:**

* Build, deploy, and operate production classification and evaluation systems that provide useful feedback to post-training and research teams.
* Optimize production inference, including model serving, batching, caching, throughput, latency, compute utilization, and cost.
* Train and optimize classifiers using supervised fine-tuning and distillation, validating quality and efficiency before deployment.
* Own the reliability and observability of deployed classifiers, diagnose production issues, and improve their performance over time.
* Design rigorous evaluations that assess accuracy, calibration, generalization, and potential measurement bias while guiding production tradeoffs.
* Partner with software engineers to integrate models into reliable execution systems and support safe rollouts.
* Build reusable engineering tools that make classifier training, evaluation, deployment, and iteration faster and more repeatable.
* Work with post-training researchers and data scientists to translate their needs into modeling objectives, evaluation criteria, and production requirements.

### **Your background might look something like**

* Experience taking ML projects from problem formulation and experimentation through production deployment.
* Strong foundations in supervised learning, statistics, and experimental design.
* Practical experience evaluating models, investigating systematic errors, and working with imperfect labels.
* Strong software engineering skills and the ability to build maintainable production ML systems.
* Experience reasoning about inference performance and the tradeoffs between quality, latency, and compute cost.
* The ability to combine careful scientific judgment with a practical approach to shipping and operating software.
* Comfort working closely with researchers and engineers on open-ended problems.
* Fluency in using AI agents to move quickly across multiple workstreams in a fast-paced environment.
* Experience with model distillation, representation learning, calibration, or efficient inference is a plus.

**About OpenAI**

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. 

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. 

For additional information, please see [OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement](https://cdn.openai.com/policies/eeo-policy-statement.pdf).

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

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We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this [link](https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241).

[OpenAI Global Applicant Privacy Policy](https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf)

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.