Machine Learning Engineer Jobs

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As a Research Engineer in OpenAI's Monetization Group, you will design and deploy advanced machine learning models to solve real-world problems, bringing research from concept to implementation and creating AI-driven applications with direct impact. You will collaborate closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Your work includes implementing scalable data pipelines, optimizing models for performance and accuracy to ensure they are production-ready, and monitoring and maintaining deployed models to ensure they continue delivering value. You will stay ahead of developments in machine learning and AI by engaging with the latest research, participate in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.

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The role involves developing machine learning and artificial intelligence systems by leveraging and extending state-of-the-art methods and architectures, designing experiments, and conducting benchmarks to evaluate and improve AI performance in real-world scenarios. The candidate will participate in impactful projects and collaborate with multiple teams and backgrounds to integrate cutting-edge ML/AI into production systems. Responsibilities also include ensuring AI software is deployed to production with proper testing, quality assurance, and monitoring.

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The role involves acting as the technical owner for enterprise customer post-training engagements, owning post-training projects end-to-end from customer requirements through delivery and evaluation. Responsibilities include translating customer requirements into concrete post-training specifications and workflows, designing and executing data generation, filtering, and quality assessment processes, running supervised fine-tuning, preference alignment, and reinforcement learning workflows, as well as designing task-specific evaluations, interpreting results, and feeding learnings back into core post-training pipelines.

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As a machine learning engineer on the core ML payments team, you will design, build, and deploy scalable machine learning solutions and systems. You will experiment with new modeling approaches and strategies, collaborate closely with a team of engineers on ingesting signals, and productionize these models. Your work will empower millions of users through well-known and emerging Fintech applications with access to financial services. Responsibilities also include working on both 0-1 stage problems and 1-10 stage problems, developing AI/ML models through the full lifecycle from offline training to online serving and monitoring, collaborating with teams across the company to define the ML roadmap, and applying data-driven decisions in day-to-day work in a high ownership, bottom-up driven team.

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As a software engineer on the perception mapping team at Zoox, you will curate, validate, and label datasets for model training and validation. You will research, implement, and train machine learning models to perform semantic map element detection and closely collaborate with validation teams to formulate and execute model validation pipelines. You will integrate models into the greater onboard autonomy system within compute budgets. Additionally, you will serve as a technical leader on the team, maintaining coding and ML development best practices and contributing to architectural decisions.

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The Machine Learning Engineer is responsible for building and deploying machine learning models that enhance sleep experiences through personalization, prediction, and behavior understanding, including readiness forecasting, event detection, and individualized recommendations. They will apply and adapt foundation-model capabilities to product workflows, develop user behavior models connecting longitudinal signals to actionable interventions, and design evaluation strategies for offline metrics, slice-based analysis, calibration, reliability, and fairness. The role involves partnering with Product teams to run high-quality online experiments, productionizing models via scalable training and inference pipelines, model monitoring, drift detection, alerting, and continuous improvement loops. Collaboration with cross-functional partners such as Product, Mobile, Backend, and Clinical teams is essential to scope requirements and deliver high-impact features.

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As an AI/ML Engineer Intern at Brain Co., you will assist in designing and deploying large language model (LLM)-powered applications to automate complex, real-world workflows. You will build and improve data pipelines and support model training, evaluation, and optimization. Your work involves handling structured and unstructured data, such as text, documents, and logs. You will also help prepare models and systems for production deployment and monitoring. Collaboration with senior engineers, AI researchers, and product teams is expected, along with learning best practices through code reviews, design discussions, and hands-on mentorship. Additionally, you will gain exposure to customer-facing and real-world constraints, including working with public-sector institutions.

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Take ownership for the full lifecycle of our models: design, training, evaluation, and deployment of our deep learning models in the space of speech recognition and NLP. Build and continuously improve deep learning models for speech recognition and natural language understanding that power our core product and help thousands of users. Develop and run large-scale self-supervised training pipelines, as well as low-latency inference systems for mobile devices.

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As a Senior Machine Learning Engineer at Neara, you will create machine learning models that drive the digitisation of real-world infrastructure from various data sources such as LIDAR, imagery, and vector data. You will work at every stage of the ML lifecycle, including data collection, quality assurance, training, and model monitoring. You will decide which problems are suitable for machine learning solutions, define the ML strategy, and stay updated with best practices in data handling, MLOps, and the latest advancements in machine learning to integrate new techniques into the platform. Responsibilities also include developing approaches to generate accurate electric networks from imperfect data using deep learning and classical ML algorithms, developing and optimizing training pipelines, scaling model serving for different problems, improving model QA speed and identifying data and distribution drift, working with diverse data sources and building scalable data pipelines for training and serving, and mentoring junior engineers in best practices for model training and software engineering.

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As a Machine Learning Research Engineer, you will be creating critical AI features for the core Archie product by working closely with AI research scientists, forward deployed engineers, software engineers, and subject matter experts to build AI capabilities for real engineering design tasks. Responsibilities include learning from experts in aerospace, electrical, mechanical, and automotive engineering to develop AI tools solving design engineering problems, collaborating with research scientists to train large language models and transition them into the core product through methods such as Mid-Training, SFT, RL, and Post-Training, and building new agentic features and integrations with major engineering design tools.