Top Machine Learning Jobs

The AI / Machine Learning Engineer will design, build, and deploy ML models for image classification, fraud detection, and data pipelines, while collaborating with product and engineering teams.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

The Staff AI/ML Engineer will lead the development of AI systems for regulatory compliance, focusing on ML architecture, implementation, and oversight of model reliability.

Design and implement reinforcement learning algorithms for robotics, conduct experiments, optimize models, and collaborate with cross-functional teams.

Design, build, and deploy machine learning solutions. Collaborate with teams to transform research into practical applications using large datasets.

Lead the development and deployment of large-scale machine learning models, optimize model performance, and enhance collaboration across teams while mentoring junior engineers.

Develop, deploy, and integrate machine learning and deep learning models for scientific and industrial problems (materials, chemistry, process optimization). Build data pipelines, feature engineering, model evaluation, and scalable production integrations. Collaborate with scientists, engineers, product, and customer teams to translate domain problems into AI solutions, support deployments, and communicate results. Conduct strategic research and stay current with AI/ML methods; periodic customer travel.

Design, develop, and deploy production-grade AI/ML models and scalable architectures. Lead model training, validation, deployment, monitoring, and cross-functional integration. Conduct research, document methods, and share knowledge to drive business impact and operational reliability.

Lead inference acceleration and GPU-parallelism optimizations (TP, SP, PP); implement high-performance CUDA/NCCL kernels; deploy videogen and LLM models to production; collaborate with researchers, conduct code reviews, mentor engineers, and optionally improve training efficiency and resource utilization.

Own end-to-end clinical prediction models: design, train, evaluate, and ship models using physiological time series and EHR data. Define labels with clinical teams, build evaluation/backtesting and regression infrastructure, assess robustness and subgroup performance, support production deployment and monitoring, and contribute to validation and regulatory evidence.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Design and deploy ML models and platforms for Axon products, collaborating with scientists and PMs to build POCs, train/evaluate/test models, enable secure privacy-preserving deployment to cloud and devices, and set standards for ethical AI.

Role involves assisting with machine learning model improvement through tasks like data labeling, content evaluation, and participation in testing activities.

Build core infrastructure and ML models for a cloud-native IDE: architect the development environment, integrate with GitHub and Linear, fine-tune models, develop always-on AI agents, and design systems that continuously learn from user behavior.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.

The Senior Machine Learning Engineer will build and optimize ML systems for CRM marketing, collaborating with cross-functional teams to impact customer engagement and product functionality.

Design, develop, and maintain AI/ML applications and tooling. Collaborate with engineers and leadership to build solutions for large-scale data, perform debugging, testing, and optimization, and produce technical documentation and user support materials.

Design, develop, and maintain AI/ML software solutions; collaborate with developers and leadership; build tools for analyzing large-scale data; debug, profile, and optimize code; create documentation and user support materials; leverage modern LLM tools to accelerate development.

Develop, debug, and maintain AI applications and tools alongside engineers. Design solutions, build data analysis tools, optimize performance, create documentation, and support users.

Design, develop, and maintain AI-driven software solutions; collaborate with teams to analyze large-scale data, debug and optimize code, build user tools, and produce technical documentation and support guides.

Design, develop, and deploy ML solutions for localization workflows (machine translation, LLM fine-tuning, QA). Own projects end-to-end, implement and optimize models using Python and ML frameworks, deploy via Docker and AWS (SageMaker/EC2/S3), monitor production, document experiments, and collaborate across teams.

Lead a Machine Learning engineering team owning mission, execution, morale, and code quality. Partner with product to deliver roadmap, remove roadblocks, improve processes, coach and grow engineers, and drive development, deployment, and innovation of ML models for recommender, search, and matching systems.

Lead end-to-end supply chain planning science: own forecasting tournaments, inventory placement optimization, vendor performance systems, and raw-material signals. Define methodology, production models, and iteration loops; drive AI-native workflows (LLM-aided EDA, agentic feature engineering), partner with planning operators and engineering, and mentor junior scientists to raise methodological standards and deliver measurable business impact.

Senior ML Engineer joins a product team to design, build, deploy, and monitor production-grade AI/ML solutions. Responsibilities include algorithm and software design, pairing with teammates, creating scalable data pipelines and infrastructure, implementing model serving and monitoring, performing performance tuning and testing, and supporting product lifecycle and stakeholder collaboration.

Design, develop, and deploy production-grade AI/ML systems (NLP/LLMs, CV, generative models). Build scalable data pipelines, microservices/APIs, and cloud/edge deployments; optimize, version, and evaluate models; integrate solutions into classified operational environments and produce technical documentation.