Top Machine Learning Jobs

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.

Lead a small team to design, build, deploy, and monitor ML/AI solutions for Claims (fraud, triage, severity, litigation, subrogation). Manage projects, collaborate with engineering and IT, follow model governance, and mentor staff.

Lead enterprise risk adjustment strategy using advanced analytics and ML to maximize risk score accuracy and reimbursement across Medicare Advantage, Medicaid Managed Care, and ACO REACH. Build predictive models, oversee data pipelines, run chart review programs, partner with clinical and finance teams, maintain audit-ready analytics, and present results to executive and board stakeholders.

Develop AI-augmented scientific models, surrogate models, and reproducible software to transform experimental and field data into engineering insights. Apply machine learning, physics-informed modeling, uncertainty analysis, and AI-assisted workflows to accelerate R&D decision-making, calibrate and validate models, and support cross-functional teams in materials and device research.

Design, develop, and deploy AI/ML models and data-driven solutions for Honeywell business groups. Collaborate with data engineers and architects to integrate models into production, define data acquisition strategies, govern analytics strategy, and promote AI/ML best practices across the Forge platform.

Lead design, development, and deployment of machine learning models to drive finance revenue and predict spending/lending patterns. Apply causal inference, advanced analytics, and visualization to produce actionable insights, collaborate with stakeholders and validation teams, and ensure regulatory compliance.

Design, build, and deploy large-scale ML systems for ad ranking, bid optimization, and recommendations. Build and operate production ML pipelines (feature engineering, distributed training, deployment, retraining, monitoring) that process billions of events and serve low-latency predictions. Optimize training and inference for compute and cost efficiency. Partner with Data Science, Data Engineering, and product teams to ship models, run online experiments, and measure CTR/CVR/ROI impact. Incorporate applied ML research and techniques into advertising and audience modeling.

Responsible for designing and optimizing VLSI circuits, translating requirements into data science problems, and building machine learning solutions. Collaborate on hardware design and integrate models into existing tools.

Lead ML-focused silicon architecture: explore algorithms/architectures for AI accelerators, build bit-accurate C-models and test platforms, drive architecture exploration, and collaborate with hardware/software teams to support design, DV, emulation, and kernel development through the silicon cycle.

The job involves developing machine learning algorithms, designing data pipelines, utilizing cloud infrastructure for ML solutions, and ensuring model governance practices.

Develop and validate machine learning detections to detect, prevent, and respond to cheating in Call of Duty. Build and improve production ML models, run experiments and analyses, manage large datasets and ETL processes, and collaborate with game designers, engineers, and stakeholders to measure impact and operationalize anti-cheat solutions.