Top Machine Learning Jobs

Build and deploy enterprise agentic AI solutions, including agents, MCP tools, plugins, orchestration architectures, evaluation harnesses, and secure APIs. Integrate agents with search and collaboration platforms, instrument production behavior, manage cost and latency, and test against prompt injection and tool vulnerabilities. The role requires backend software engineering, production agent development, MCP experience, LLM evaluation, and AI coding assistant expertise, with preferred experience in managed runtimes, retrieval systems, and enterprise security.

Lead development and deployment of an agentic AI/ML pipeline for identifying, prioritizing, and pricing high-cost healthcare claims. Responsibilities include model development, confidence scoring, HITL safety design, ML lifecycle management, monitoring, workflow automation, responsible AI, and enterprise-scale analytics using Databricks and Snowflake. The role also evaluates emerging AI technologies and supports regulatory-aware automation and healthcare data transformation.

Designs, deploys, and scales AI and machine learning solutions for enterprise healthcare applications. Responsibilities include building and tuning production models, developing MLOps frameworks and deployment pipelines, monitoring model performance, automating workflows, evaluating emerging AI technologies, and ensuring security, scalability, responsible AI, and governance standards. The role collaborates with software engineers, data scientists, and business stakeholders.

Designs, deploys, and maintains enterprise AI and machine learning solutions in cloud production environments. Builds scalable models, data pipelines, ML architectures, and MLOps workflows; monitors model performance; evaluates emerging AI technologies; and collaborates with data scientists, engineers, and product leaders on healthcare solutions. The role also includes code reviews, technical mentoring, responsible AI practices, and continuous improvement.

Develop and deploy advanced machine learning models across asset classes, focusing on deep learning, sequence/time-series modeling, NLP/LLMs, pre-training, fine-tuning, and reinforcement learning. Design and optimize models for performance, scalability, and robustness to model price formation, market behavior, and risk while collaborating with researchers, engineers, and traders.

Designs, develops, and troubleshoots secure, scalable ML software systems and services. Builds production code, architecture artifacts, APIs, and distributed microservices; analyzes data to improve applications and system architecture. Uses AI-assisted development tools responsibly, validates generated outputs, and guides peers on secure engineering practices. Requires Python and ML framework expertise, cloud-native technologies, public cloud experience, distributed systems knowledge, and Cassandra or equivalent NoSQL experience. Preferred experience includes GPU workloads, model serving, distributed inference, model compression, and edge deployment.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead a team of ML engineers building and operating scalable risk features, data pipelines, supervised and unsupervised models, LLM solutions, and explainability systems for financial crime monitoring. Responsibilities include productionizing ML applications, monitoring drift and bias, optimizing performance, developing Databricks pipelines, and collaborating with product, SRE, compliance, and operational teams.

Lead software engineers and applied AI/ML practitioners while developing secure, scalable production systems in Java or Python. Integrate LLMs, generative AI, NLP, RAG, and coding assistants into software engineering workflows. Establish responsible AI governance, validation standards, testing, security, and operational practices. Guide system architecture, experiments, algorithm implementation, productionization, code quality, automation adoption, and technical decision-making across global teams.

Build and deploy scalable regulatory model applications and cloud modernization solutions using PySpark, Python, Databricks, and AWS. Design secure, resilient architectures; develop distributed data processing systems; write tested, documented production code; support reliability and observability; and collaborate with product, data, model, and platform teams. The role may also enable AI agent development through LLM workflows, orchestration, evaluation, and guardrails.

Build and support scalable, production-grade regulatory model applications and cloud modernization solutions. Design distributed data processing systems using PySpark, Python, and Databricks; implement secure and resilient AWS architectures; write tested, documented code; and contribute to architecture reviews and release readiness. Collaborate with product, data, model, and platform teams. Experience enabling AI agent development, including LLM workflows, orchestration, evaluation, and guardrails, is a plus.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.

Leads technical architecture for software, AI/ML, and cloud systems supporting aircraft readiness. Designs, deploys, and improves machine learning models for predictive maintenance; performs data engineering and feature extraction; modernizes legacy applications; builds secure cloud infrastructure, CI/CD pipelines, containers, and infrastructure-as-code solutions. Integrates language models, supports RMF and ATO compliance, enforces security and privacy standards, and mentors junior technical staff while serving as the primary technical contact for government stakeholders.

Build and ship production machine learning and computer vision systems processing 11 million daily images. Own the full lifecycle from sensor data collection, dataset curation, training, and deployment through monitoring and iteration. Develop detection, segmentation, classification, embedding, and re-identification models for cloud and roadside edge hardware, balancing accuracy, speed, and power efficiency. Apply both classical computer vision and state-of-the-art methods while writing production-quality software.

Develop and deploy physical AI systems on real robots, including learned perception, vision-language-action models, imitation and reinforcement learning, world models, autonomous behavior, and sensor-data pipelines. Prototype approaches in simulation and field environments, optimize models for latency and edge deployment, and collaborate across robotics, controls, embedded, mechanical, and product engineering teams. The role also contributes to long-term Physical AI architecture and research direction.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

Design, optimize, and deploy machine learning models for mobile, embedded, and edge devices. Responsibilities include model compression, quantization, pruning, hardware-aware performance tuning, cross-platform inference, secure model updates, hybrid edge-cloud architectures, privacy-aware telemetry, benchmarking, and collaboration with hardware, firmware, and product teams. The role also requires maintaining technical documentation and evaluating emerging edge AI technologies.

Lead analytics and ML work to drive Aetna Medicare growth: design experiments, segmentation, forecasting, and end-to-end ML system architecture; prototype models, collaborate with engineering and product teams for production deployment, and communicate data-driven recommendations to cross-functional stakeholders.

Develop statistical and machine learning techniques, quantitative models, and scalable automation for evaluation pipelines. Build data-processing infrastructure, improve evaluation efficiency, and solve ambiguous problems across statistics, ML, and large-scale systems. Collaborate with engineers, data scientists, product managers, and leadership while maintaining code quality in a complex codebase.

Design and lead cloud and in-car data mining strategies for autonomous vehicle perception and end-to-end models. Build AI-driven software to accelerate data mining, collection, delivery, and corner-case resolution. Develop operational processes using big data, statistics, and machine learning to ensure high-quality training data from production and collection vehicles. Collaborate with platform teams and balance cost, engineering quality, and delivery timelines.