Top Machine Learning Engineer Jobs

Build, deploy, scale, monitor, and maintain production machine learning models and infrastructure. The role involves developing ML components, optimizing algorithms and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, and applying responsible AI and model governance practices. Candidates collaborate with product, data science, and Agile engineering teams while using technologies such as Python, PyTorch, Spark, Kubernetes, and major cloud platforms.

Build, deploy, scale, and operate production machine learning models and platforms for risk management. Responsibilities include developing ML applications, distributed training and serving systems, cloud architectures, data pipelines, model monitoring, retraining, testing, CI/CD, governance, and responsible AI practices. The role collaborates with Product, Data Science, GRC, and cross-functional Agile teams while solving complex business problems with advanced algorithms and open-source frameworks.

Build and improve ML platform tooling, infrastructure, workflows, and observability for machine learning models, LLMs, and agentic applications. Collaborate with ML and infrastructure teams, evaluate and integrate open-source tools, automate fine-tuning and evaluation pipelines, and debug distributed systems across infrastructure, networking, and application layers.

Build and improve Kensho’s ML platform, tooling, infrastructure, and workflows. The role supports ML engineers by developing scalable, auditable processes for model fine-tuning, reinforcement learning, LLM and agent evaluation, and production observability. Responsibilities include integrating open-source solutions, debugging distributed systems, monitoring model and agent performance, and promoting engineering best practices across teams. The position requires collaboration across ML, infrastructure, SRE, and security teams.

Develop and productize machine learning and digital signal processing algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Research modern deep learning approaches, prototype with Python or MATLAB, and deploy optimized real-time solutions on consumer hardware. Collaborate across research, systems engineering, and product development to deliver robust audio technologies at scale.

Develop and productize advanced audio DSP and machine learning algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Translate research into scalable, optimized, real-time solutions for consumer devices and production hardware. Balance audio quality, ML performance, and system constraints while influencing product direction. Collaborate across algorithm research, systems engineering, deployment, and productization.

Own and build Confido's ML platform: design and operate end-to-end ML pipelines, infrastructure-as-code and CI/CD, optimize training/inference and agent workloads for latency/cost/throughput, integrate model data flows with data engineering, and ensure reliability, observability, security, and production model quality with online evals and human-in-the-loop review.

Lead development and production deployment of machine learning systems for time-series anomaly detection in connected-device environments. Build streaming and MLOps pipelines, tune alerts, monitor model drift, and integrate AI systems with LLM-based assistants. Guide architecture, tooling, observability, and production readiness while mentoring and managing applied AI and full-stack engineers. Collaborate across engineering, product, data, and research teams.

Build and operate large-scale ML infrastructure, including feature pipelines, model training and evaluation systems, high-performance inference services, data management platforms, and model orchestration tools. The role partners with ML engineers to deploy models into production, improve system reliability and efficiency, and maintain secure, scalable, production-ready services using Python, Java, and related AI technologies.

Build and operate large-scale machine learning inference infrastructure for recommendation models, embedding-generation models, and self-hosted LLMs. Responsibilities include model optimization, export, serving, hosting, LLM fine-tuning, reinforcement learning infrastructure, and inference performance improvements. The role supports highly available ML systems across Snap’s advertising, content, growth, camera, and other product teams.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

Build and maintain AI foundations and production model services, including fine-tuning and deploying open-source models in GPU-backed infrastructure. Develop computer vision, image understanding, OCR, document layout analysis, and multimodal solutions for PDFs, images, and scanned documents. Optimize inference performance, resource utilization, throughput, and latency while debugging production systems and improving engineering standards.

Develop and deploy computer vision machine learning models for customer applications. Responsibilities include data preparation, model training and fine-tuning, computer vision algorithm development, model optimization for edge hardware, evaluation, production monitoring, drift diagnosis, and retraining. The role involves collaborating with customers, product teams, and solutions engineers, supporting on-site deployments, and contributing to shared ML tooling.

Design, develop, test, and deploy AI/ML services and agentic workflows for rugged tactical networking and computing systems. Translate business needs into technical requirements, collaborate with technical owners, and build scalable applications using Python, Linux, cloud-native infrastructure, AI frameworks, databases, observability tools, and CI/CD pipelines. The role supports mission-critical government, defense, and intelligence applications.

Designs scalable backend services, distributed systems, event-driven data pipelines, and production machine learning models. Responsibilities span feature engineering, model training, deployment, monitoring, real-time decision engines, microservices, cloud platforms, and AI/LLM workflow orchestration. The role requires collaboration across engineering, data, and product teams and is performed on-site in Manchester, New Hampshire, with no relocation available.

Leads the architecture and technical execution of Cisco’s distributed AI platform. Designs high-concurrency Go and Python microservices, agentic AI orchestration, identity and multi-tenant systems, API gateways, and secure service communication. Authors technical specifications, drives zero-trust security, CVE remediation, production triage, and platform reliability. Mentors senior engineers and establishes engineering standards across teams.

Build and deploy computer vision models for live and recorded video feeds. Responsibilities include curating and annotating datasets, training and tuning YOLO models, processing video with Python, developing inference pipelines, evaluating model performance, and delivering production-ready code in an Agile environment. The role supports a federal border security mission and requires onsite work in Ashburn, Virginia, U.S. citizenship, and successful completion of a CBP background investigation.

Technical Lead responsible for architecting, deploying, operating, and sustaining secure agentic AI, LLM inference, data, and platform capabilities across classified and unclassified environments. Responsibilities include JWICS hosting, cross-domain data flows, cloud architecture, observability, security hardening, integration and performance testing, troubleshooting, documentation, accreditation support, and technical leadership. The role is onsite at the Pentagon and requires routine domestic and international travel.

Own the end-to-end computer vision platform for fine-grained item identification and pricing. Responsibilities include designing and fine-tuning vision models, defining evaluation and confidence standards, building labeling feedback loops, prioritizing coverage versus accuracy, and deploying reliable, low-latency serving infrastructure at scale. The role also communicates model capabilities and trade-offs to technical and non-technical stakeholders.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, hybrid search, model integration, evaluation, observability, safety guardrails, and governance. The role provides architectural leadership, mentors engineers, leads design reviews, and develops scalable AI infrastructure across hyperscalers and company platforms.

Designs, develops, deploys, and optimizes generative AI and machine learning solutions on AWS. Builds Amazon Bedrock and RAG applications, document-processing bots, APIs, and scalable ML models. Responsibilities include model evaluation, monitoring, troubleshooting, continuous improvement, data extraction, and cross-functional AI integration. The role requires expertise in Python, cloud architecture, OCR technologies, NLP, deep learning, CI/CD, and web application development.

Supports machine learning development for fraud detection, including data preprocessing, feature engineering, model training, evaluation, experiment tracking, drift monitoring, and batch scoring. Maintains Python data pipelines, Jupyter reporting artifacts, and MLflow documentation. Assists with NLP/NER transformations and UiPath Maestro integration testing while following government security, data governance, CI/CD, and EPLC documentation requirements.

Lead end-to-end machine learning solutions for League of Legends, from problem framing to production. Build personalization, recommender, matchmaking, and in-game ML systems; design evaluations and experiments; integrate models with game and service engineering; create reusable ML patterns; mentor engineers and improve ML observability, monitoring, and launch practices.

Lead the design, development, optimization, and production deployment of advanced machine learning models and algorithms. Build scalable ML pipelines, preprocess and analyze large datasets, ensure data quality, monitor model performance, and collaborate with data scientists, software engineers, and product teams to integrate AI/ML solutions into products and services.

Lead the architecture and production operation of Grubhub’s ranking, recommendation, retrieval, and query-understanding systems. Drive improvements in long-term diner value through deep learning, embeddings, LLMs, experimentation, and real-time inference. Establish model evaluation, deployment, observability, and reliability standards across teams. Partner with Product, Search, Ads, and Data Platform, mentor engineers, influence technical strategy, and communicate trade-offs to executive stakeholders.