Staff Machine Learning Engineer
Design, train, test, and iterate on diffusion models for 3D geological models. Design, train, test, and iterate on an approach for conditioning generation on geophysical data and other observations. Inform the generation of synthetic data to improve model performance. Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.
Staff Machine Learning Engineer
Define Adaptive's ML strategy including where ML should be applied across products, required infrastructure, and build vs. buy decisions. Design and build production ML systems end-to-end including data pipelines, model training, evaluation frameworks, and inference serving. Establish evaluation methodology to measure model quality, catch regressions, and make data-driven decisions about model changes. Own the strategy for acquiring and formatting necessary data, including labeling, feedback loops, and model improvement over time. Partner with product engineers to integrate ML into the product by writing production code and working within existing codebase. Help build and lead the ML team as scope grows.
Machine Learning Engineer, Distributed Data Systems
Design, build, and maintain data infrastructure systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure, and machine learning infrastructure while ensuring scalability, reliability, and security. Ensure the data platform can scale by orders of magnitude while remaining reliable and efficient. Partner with researchers to deeply understand requirements and translate them into production-ready systems. Harden, optimize, and maintain critical data infrastructure systems that power multimodal training and evaluation.
Member of Technical Staff - ML Engineering
Deploy, maintain, and optimize production and research compute clusters. Design and implement scalable and efficient ML inference solutions. Develop dynamic / heterogeneous compute solutions for balancing research and production needs. Contribute to productizing model APIs for external use. Develop infrastructure observability and monitoring solutions.
Member of Technical Staff - Post Training, Applied
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.
Senior Machine Learning Engineer - Payments
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.
Machine Learning Engineer - Perception Mapping (copy)
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.
Machine Learning Engineer (Foundation Models & Personalization)
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.
AI/ML 2026 Internship
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.
ML Engineer - NLP (m/f/d)
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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