Top Machine Learning Engineer Jobs

Lead development and production deployment of end-to-end driving, VLA, and world-model systems for L4 autonomous vehicles. Define scalable autonomy architectures, adapt foundation models, improve training and evaluation efficiency, and drive technical initiatives from concept to deployment. The role also includes mentoring engineers, shaping technical direction, and contributing to the founding autonomy team.

Lead the architecture, deployment, and optimization of scalable machine learning serving systems for real-time and batch workloads. Build CI/CD pipelines, monitoring, model versioning, and reliable MLOps infrastructure. Manage AWS cloud infrastructure, collaborate with data scientists and engineering teams, and support production systems through on-call rotation. The role also involves improving observability, reproducibility, security, and deployment reliability, with opportunities to deploy LLMs and distributed ML systems.

Design and build reinforcement learning environments, evaluation systems, reward functions, datasets, data pipelines, and ML infrastructure for computer-aided design tasks. The role converts expert mechanical and electrical engineering data into reliable model-training assets, automates scoring and content workflows, and runs experiments guiding technical and product decisions. Candidates need machine learning experience, physical-engineering domain exposure, engineering design software or vision-based ML familiarity, and hands-on reinforcement learning environment experience.

Own performance optimization for foundation-model training across thousands of accelerators. Diagnose and resolve bottlenecks across data paths, kernels, parallelism, communication, observability, and long-running stability. Partner with modeling, infrastructure, simulation, data-mining, and robotics teams; establish reusable performance improvements and technical direction. Build profiling infrastructure and agentic automation to reproduce jobs, verify numerical parity, detect regressions, and automate optimization workflows.

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.

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.

Leads software engineering within JPMorganChase’s Payments organization, designing, developing, testing, and troubleshooting secure, scalable production systems. The role creates architecture and design artifacts, maintains algorithms, analyzes large datasets, improves system architecture and coding practices, and uses enterprise-approved AI-assisted development tools responsibly. Required experience includes Java, Python, SQL, PostgreSQL, AWS, SDLC, agile methods, CI/CD, resiliency, and security. Databricks and machine learning frameworks are preferred.

Leads development of secure, scalable Java and AWS software systems, including microservices, event-driven platforms, and production machine-learning systems. Designs data and feature pipelines, model deployment and monitoring, automation, CI/CD, and operational stability practices. Guides code reviews, testing, secure coding, vendor evaluations, and responsible adoption of AI-assisted engineering tools. Collaborates across product, data, platform, SRE, and governance teams while promoting MLOps, LLM, observability, and model-monitoring practices.

Build and operate scalable machine learning training platforms and pipelines across AWS and other cloud environments. Optimize GPU-based single-node and distributed training for performance, cost, stability, and reproducibility. Develop Kubernetes infrastructure, GenAI and LLM fine-tuning workflows, observability, CI/CD automation, governance controls, and developer self-service tools. Partner with data engineering and platform teams while applying secure, responsible AI-assisted development practices.

Design, prototype, and productionize large language models and architectures for heterogeneous financial data. Implement pre-training objectives, optimize training/serving at scale, mentor engineers, and collaborate across teams to deliver reliable, secure, and interpretable ML systems.

Design, implement, and maintain reliable, scalable cloud infrastructure and SRE practices for critical applications. Build IaC, CI/CD pipelines, observability, and incident response; apply enterprise-authorized AI to accelerate triage while validating outputs and protecting sensitive data.

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 design, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.

Build, train, deploy, and optimize production-scale machine learning models and neural networks. Design scalable data pipelines, distributed training systems, backend improvements, and high-throughput inference solutions. Conduct metric-driven research, improve model accuracy and performance, maintain secure and high-quality code, mentor junior engineers, and collaborate cross-functionally. The role focuses on deep learning, computer vision or NLP, and taking ML products from concept through production.

Develop and productionize machine learning models at scale, including neural network design, data preparation, training, tuning, deployment, monitoring, and continuous improvement. Build scalable data pipelines and secure backend systems, conduct metric-driven research, improve engineering practices, mentor junior engineers, and collaborate cross-functionally. The role requires expertise in modern ML frameworks, distributed systems, Python or shell scripting, and a machine learning specialization such as computer vision or NLP.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security missions. Integrates data pipelines, evaluates model performance, ensures data and output quality, and transforms prototypes into scalable production solutions. Requires strong expertise in machine learning algorithms, software architecture, data engineering, model validation, and system integration. The role may lead distributed teams and requires an active TS/SCI clearance with polygraph.

Build and deploy GenAI, ML, RAG, and agentic systems while conducting offensive security research and red teaming against AI applications. Identify prompt injection, data leakage, model theft, adversarial attacks, and other vulnerabilities; develop scanning and fuzzing tools; and implement secure-by-design hardening strategies. The role also involves cross-functional collaboration, technical research and writing, threat intelligence monitoring, community representation, and mentoring engineers.

Secure AI, machine learning, inference, and developer platforms through threat modeling, architecture and code reviews, secure API and distributed-system design, model and supply-chain security, and security automation. Build controls for authentication, authorization, isolation, rate limiting, monitoring, anomaly detection, CI/CD scanning, and protection against prompt injection, model extraction, adversarial inputs, and data leakage. Partner closely with ML, platform, infrastructure, engineering leadership, and the CISO.

Build and operate cloud-based MLOps platforms for analytical and generative AI models. Develop automated pipelines for model training, deployment, monitoring, governance, and lifecycle management. Partner with data science, engineering, security, compliance, and business teams to establish standards and deliver scalable, secure, production-ready AI solutions. Research and prototype emerging MLOps capabilities while connecting technical delivery to business and financial outcomes.

Develop and deploy production AI/ML and generative AI solutions for FDA modernization initiatives. Build RAG pipelines, LLM applications, machine learning models, APIs, data pipelines, and MLOps/LLMOps capabilities. Evaluate model quality, implement responsible AI safeguards, monitor production systems, and integrate AI services into enterprise applications. Collaborate with data, software, cloud, and product teams in an Agile environment while documenting solutions and mentoring less-experienced engineers.

Leads client-facing discovery and technical product development for AI initiatives. Designs, prototypes, deploys, and operates agentic systems, retrieval pipelines, enterprise integrations, and production-grade generative AI applications. Establishes evaluation, observability, safety, governance, and reliability frameworks while translating business needs into technical specifications. Requires strong Python and software engineering expertise, cloud and DevOps experience, retrieval architecture knowledge, and at least seven years of engineering experience, including production generative AI experience.

Lead client-facing AI discovery and technical product leadership while designing, prototyping, and productionizing agentic systems, retrieval applications, and enterprise AI workflows. Build full-stack software, evaluation and observability frameworks, safety guardrails, and cloud deployments. Translate business needs into technical specifications, prioritize use cases, and guide solutions from rapid proof of concept through secure, reliable production systems.

Develop machine learning systems to detect AI misuse, anomalous behavior, coordinated attacks, and agentic risks. Build synthetic data pipelines, classifiers, monitoring systems, threat models, evaluations, and mitigations for prompt injection and other harms. Conduct research in automated red-teaming and adversarial robustness while taking projects from exploratory research through production deployment and communicating technical findings to nontechnical stakeholders.

Lead the architecture and development of production AI/ML systems, including LLM applications, RAG, tool use, multi-agent workflows, and real-time inference. Partner with product, design, data, and leadership to ship scalable capabilities. Implement AI security, safety, privacy, reliability, observability, and human-in-the-loop controls. Mentor engineers and help shape technical practices in a fast-moving fintech startup.