Top Machine Learning Engineer Jobs

Designs, develops, tests, and optimizes AI and machine learning systems across business problems. Applies machine learning, deep learning, transformer architectures, statistical reasoning, and NLP to build models and pipelines. Uses cloud platforms and distributed computing, collaborates with MLOps engineers on deployment, and follows software engineering practices. Responsibilities span the data science lifecycle, including data preparation, feature extraction, model development, deployment, maintenance, and communication with nontechnical stakeholders.

Build and improve ML and data pipelines, ship full-stack product features, and enhance Mac and Windows desktop applications. Work directly with enterprise customers to clarify requirements, deploy solutions, and turn customer learnings into repeatable product improvements. The role requires high ownership, rapid execution, strong engineering practices, and measurable customer outcomes across applied ML, AI systems, product, and infrastructure.

Operate and assure production AI/ML applications, foundation-model integrations, and agentic workflows. Monitor reliability, latency, errors, costs, usage, and workflow performance; investigate failures; maintain runbooks; and conduct evaluations and regression testing. Manage prompts, models, tools, retrieval sources, configurations, deployments, and rollbacks. Support cybersecurity, audit, access control, incident response, compliance evidence, and operational improvements for secure, reliable, and auditable AI services.

Designs and maintains enterprise AI/ML platform capabilities, managed model integrations, reusable APIs, agentic workflows, developer assistants, and secure automation. Integrates AI services with cloud platforms, identity controls, secrets management, observability, and enterprise tools. Develops tests, evaluates model and agent updates, troubleshoots distributed workflows, and collaborates with platform, DevSecOps, cybersecurity, and operations teams in secure and restricted environments.

Early-career engineer responsible for characterizing AI/ML training and inference workloads, analyzing compute and memory bottlenecks, and building performance models for hardware and software systems. The role partners with architecture, compiler, runtime, and product teams on optimization decisions, benchmarking, and reproducible technical analyses. Candidates apply mathematical reasoning, computer architecture knowledge, Python-based data analysis, and machine learning frameworks. Travel is required up to 10%.

Develop and maintain scalable infrastructure for deploying AI and machine learning models. Build automation for model evaluation and deployment, optimize inference performance, and implement monitoring and alerting systems. Troubleshoot infrastructure issues, collaborate with ML scientists and cross-functional teams, and contribute to code reviews, design discussions, and architecture planning. Requires a relevant master’s degree, at least one year of related experience, and training or experience with Python, machine learning, generative AI, SQL, system programming, and cloud computing.

Build and operate distributed GPU training platforms for foundation models. Run and debug multi-node PyTorch and Ray Train workloads, tune GPU networking and scheduling, profile performance, and develop benchmarks and evaluation harnesses. Partner with infrastructure teams and client scientists to improve throughput, reliability, cost efficiency, observability, and end-to-end training pipelines on GKE.

Build and operate end-to-end machine learning platforms for maritime defense manufacturing. Responsibilities include training and deployment, model serving across cloud, edge, and factory environments, AI workflow development with human review, PLM/MES/ERP integration, governance, drift monitoring, and on-call support.

Build and operate machine-learning training and dataset infrastructure for autonomous perception systems. Responsibilities include multi-sensor data indexing, developer tools and SDKs, frontier-model applications for data curation and synthetic data, training-job and evaluation tracking, and model versioning. The role requires strong Python, distributed training experience with TensorFlow or PyTorch, and Kubernetes-based workflow orchestration. Rust, experiment tracking, and production-scale model deployment are additional advantages.

Build next-generation voice AI systems for consumer and business applications. Develop persistent memory, complex reasoning, speech pipelines, and production-grade agentic workflows. As an early technical contributor, the ML Engineer will help shape core architecture, take ambiguous concepts through production, and influence user interactions with advanced language models in a fast-paced pre-seed startup.

Lead development of real-time machine learning and computer vision algorithms for ultrasound imaging in Intuitive’s robotic-assisted biopsy system. Responsibilities include detection, segmentation, classification, temporal tracking, artifact mitigation, image fusion, model optimization, and deployment into high-performance C++ medical-device software. The role also supports system integration, testing, formal design documentation, and patent applications while collaborating with clinical and engineering teams under Good Machine Learning Practice principles.

Designs and implements AI and machine learning capabilities for cyber and intelligence workflows. The role translates mission-user processes into LLM-powered, agentic, or hybrid solutions; integrates AI with APIs, enterprise data, and mission platforms; and ensures reliable, secure production deployment in classified environments. Collaboration with analysts, operators, developers, and leadership is central. Required qualifications include Python, TS/SCI with polygraph, and relevant AI/ML or software engineering experience.

Lead end-to-end machine learning architecture, production systems, forecasting, probabilistic modeling, experimentation, and agentic AI development. Build models for acquisition cost, lifetime value, ROAS, demand, and market outcomes; establish reproducible data and model pipelines; create evaluation harnesses for LLM agents; and own calibration, feedback loops, measurement, and cost controls. Mentor cross-disciplinary engineers and influence technical strategy across teams.

Develop and deploy production-grade AI/ML solutions on Microsoft Azure. Build secure data pipelines, prepare and validate datasets, support NLP and machine learning workloads, and implement MLOps practices including CI/CD, monitoring, troubleshooting, and model lifecycle management. Collaborate with engineers, analysts, and stakeholders in Agile teams while producing architecture, testing, security, and operational documentation for regulated government environments.

Develop and improve computer vision systems for aerial image recognition, including object detection, segmentation, classification, and pattern recognition. Train, fine-tune, evaluate, and deploy machine learning models; build data pipelines; experiment with foundation and multimodal models; prototype new AI applications; and optimize model accuracy, reliability, performance, and inference efficiency. The role requires independent ownership from research through production deployment.

Develops personalized advertising recommendation algorithms using machine learning and deep learning. Responsibilities include identifying modeling opportunities, defining research roadmaps, developing and validating new models with large-scale user data and compute, analyzing metrics, and partnering with engineering and product teams to productionize research and deliver measurable business results.

Lead the development of ads recommendation and personalization models using machine learning, deep learning, user behavior data, and large-scale computing. Responsibilities include defining modeling opportunities and roadmaps, conducting foundational research, validating new models, analyzing metrics and data, and partnering with engineering and product teams to productionize research and deliver measurable business results.

Build, deploy, and operate production AI applications and scalable machine learning solutions. Develop automated training and deployment workflows, support production AI infrastructure, and collaborate with software, data science, and platform engineering teams. The role involves Python development, ML frameworks, containerization, Kubernetes, Linux, and Git, with preferred experience in LLMs, RAG pipelines, model serving, vector databases, frontend development, and ML orchestration.

Lead the technical direction for ML-powered experimentation and predictive insights. Build production systems spanning modeling, LLMs, retrieval, simulation, statistical methods, data pipelines, backend services, and product workflows. Develop traceable, calibrated, privacy-aware predictions; establish rigorous evaluation, backtesting, monitoring, and abstention practices; and translate insights into approval-gated experimentation workflows. Partner with engineering, product, data science, and research teams while providing technical leadership for high-quality production ML.

Develop and maintain Retrieval-Augmented Generation pipelines, LLM integrations, REST APIs, and self-hosted LLM deployments. Conduct LLM alignment red teaming, independently research emerging AI/ML methods, and develop advanced AI applications. The role requires strong Python and AI/ML framework expertise, with preferred experience in model evaluation, fine-tuning, inference optimization, vector databases, AWS, Docker, and scalable data architectures.

Own the quality and behavior of AI-generated interactive content. Build evaluation systems using production creator data, develop agentic ML systems that turn ideas into playable games, debug failures across models and pipelines, run rigorous experiments, and help establish the AI team’s technical standards and direction.

Design, train, and deploy machine learning and perception models for edge-intelligent maritime systems. Optimize inference pipelines for embedded hardware, develop datasets and labeling strategies, support computer vision, signal processing, and sensor-fusion experiments, and build real-time on-device and cloud data pipelines. Create benchmarking and debugging tools, evaluate emerging ML research, and collaborate across hardware, software, and product teams to deliver robust production AI solutions.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Lead ML training performance for large-scale VLM, VLA, foundation-model, and reinforcement-learning systems. Design distributed parallelism, mixed-precision strategies, gradient checkpointing, GPU kernels, profiling workflows, and performance benchmarks. Own RL optimization strategy and roadmap, diagnose bottlenecks across compute and networking layers, partner with research and engineering teams on architecture, and mentor engineers.