Top Machine Learning Engineer Jobs

As a Senior AI/ML Platform Engineer, you will architect and scale the ML platform, ensuring efficient ML workflows and collaborating with various teams to drive AI adoption.

Supports payer modernization and health services consulting engagements by analyzing operational challenges, optimizing processes, applying business analytics, engaging stakeholders, implementing quality standards, and guiding transformation initiatives. Develops operational support frameworks, conducts market research, manages change, and advises clients on improving service delivery and business performance while mentoring junior team members.

Build and optimize high-volume, real-time inference systems for speech and language models. Responsibilities include improving model serving with quantization, batching, caching, attention, and decoding techniques; developing high-performance GPU systems; scaling distributed inference across Kubernetes and multi-node environments; profiling and optimizing code; containerizing models; and ensuring reliable production operation.

Develops search relevance solutions for an enterprise question-answering platform. Responsibilities include creating and optimizing ranking features, building frameworks for diversified ranking, enabling parallel ranking experiments, and developing metrics to evaluate search performance. The role applies information retrieval, machine learning, text processing, and natural language understanding to address lexical gaps, semantic understanding, and data sparsity while collaborating with product, UX, customer success, and engineering teams.

Develop production-grade machine learning models and infrastructure to evaluate autonomous vehicle behavior in simulation and real-world environments. Collaborate across teams to deploy state-of-the-art ML systems, improve model quality, and support large-scale data and simulation workflows.

Own and lead the development, migration, and operation of recommendation systems for Fable and Everand. Responsibilities include moving Fable’s recommender to the Orion ML platform, extending ranking and retrieval capabilities, incorporating social signals, managing production quality and reliability, designing A/B tests, partnering with product managers, leading cross-team delivery, and improving engineering practices.

Develop and deploy production-grade AI and machine learning capabilities for national security and OPIR missions. Responsibilities include signal-processing automation, data processing, model development, evaluation, MLOps workflows, GPU optimization, streaming-data integration, and scalable deployment in operational environments. The role involves collaboration with engineers, mission subject-matter experts, and product teams, with emphasis on AI agents, NLP, LLMs, computer vision, microservices, and classified cloud environments.

Design, develop, and deploy production-grade AI/ML solutions for national security missions, including NLP, LLMs, generative AI, deep learning, and computer vision. Build scalable data pipelines, APIs, microservices, MLOps workflows, and cloud or edge-deployed systems. Optimize and evaluate models, integrate them into secure operational environments, and collaborate with engineering and mission teams. Document technical designs and support accreditation processes in classified settings.

Design and develop machine learning algorithms for target ROAS, automatic bidding, and ads pacing. Explore bid landscapes, optimize advertiser value and revenue, conduct A/B experiments, and collaborate with product, sales, and engineering teams on new ad products and bidding strategies. Provide staff-level technical leadership, drive cross-team alignment, and use AI-assisted development and productivity tools to improve engineering workflows.

Supports scalable ETL/ELT pipelines, cloud data platforms, database integration, machine learning model development and deployment, AI-enabled workflows, and analytics dashboards. Uses Python, data science libraries, AWS, GCP, Azure, SQL and NoSQL databases, Looker, Tableau, and automation tools. Collaborates with data engineers, analysts, data scientists, and marketing teams to deliver data-driven solutions. The role is hybrid in Dallas, requiring two in-office days weekly.

Develop and deploy machine learning models for autonomous truck behavior systems using imitation learning, reinforcement learning, and sequence modeling. Build production ML code, training pipelines, data workflows, evaluation tooling, and inference systems. Analyze model performance and failure modes, collaborate with autonomy and simulation teams, and integrate learned behavior models into simulation and validation workflows.

Own the production lifecycle of ranking and recommendation models, including training, validation, deployment, monitoring, retraining, versioning, rollback, and retirement. Build scalable Spark-based feature and data pipelines, orchestration workflows, model hubs, observability, CI/CD, and reliability standards. Partner with data science and engineering teams to improve model quality, deployment speed, infrastructure efficiency, and operational resilience while mentoring teammates.

Serves as chief architect for Sony Interactive Entertainment’s Data & Intelligence organization. Defines enterprise data and machine learning architecture, standards, reusable platforms, governance, experimentation, activation, and AI lifecycle practices. Chairs architecture reviews, leads complex cross-organizational initiatives, advises executives, removes technical blockers, and mentors senior technical professionals. The role is hands-on, requiring architecture authorship, prototyping, code and design reviews, and delivery of scalable data, ML, and decisioning systems serving millions of consumers.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Build and deploy spatial AI capabilities for generative home floor plans. Responsibilities include training and adapting ML models, creating APIs and evaluation pipelines, improving reproducibility, managing latency and cost, extending research into product features, partnering on MLOps and deployment, and mentoring engineers and researchers.

Designs, deploys, and operationalizes scalable AI microservices, MLOps workflows, production APIs, and ETL/ELT pipelines in secure cloud environments. Responsibilities include model monitoring, drift detection, automated retraining, data orchestration, CI/CD, ATO accreditation, architecture documentation, and stakeholder communication. The role supports Department of War data initiatives that improve situational awareness, operational readiness, and mission execution.

Leads the design and production deployment of machine learning algorithms for advertiser campaign optimization. Responsibilities include multi-objective optimization, bidding and budget decisions, exploration-exploitation strategies, long-term impact measurement, launch metrics frameworks, and LLM-based agentic systems for campaign diagnostics and recommendations. The role also provides technical leadership across ML, Product, and Engineering while mentoring engineers and delivering measurable impact from high-volume, real-time systems.

Develop AI/ML-powered control systems, digital twins, simulation environments, and reduced-order models for data center cooling equipment. Build and validate machine learning, reinforcement learning, and predictive maintenance solutions; deploy inference pipelines on Linux platforms; maintain FMU packages; and integrate equipment and simulations through industrial protocols including MQTT, Modbus, CAN, and REST APIs.

Develop AI/ML and LLM solutions by building ETL pipelines, curating evaluation data, implementing machine learning algorithms, and improving scalable infrastructure. The role collaborates with researchers and developers, supports NLP and predictive analytics, applies Agile methodologies, and may lead interdisciplinary teams. Work includes data engineering, cloud and containerized deployment, GenAI operations, and algorithm implementation across varied customer missions.

Design and integrate production AI/ML systems for USCYBERCOM, including LLM agents, orchestration workflows, RAG pipelines, vector databases, MCP tool integrations, model routing, evaluation pipelines, and governance guardrails. Translate research prototypes into secure, enterprise-scale mission capabilities while benchmarking models, optimizing performance, and supporting rapid operational integration.

Architect and operate Cisco’s AI Compute Platform, developing distributed control-plane services, Kubernetes platforms, GPU schedulers, APIs, controllers, and automated remediation systems. The role manages multi-cluster environments, tenant quotas, workload routing, GPU allocation, and reliability for large-scale training and inference workloads. It also leads architecture reviews, establishes engineering standards, mentors engineers, and drives Infrastructure as Code, GitOps, CI/CD, observability, incident response, and operational excellence.

Build and maintain production ML and data pipelines, optimize Spark workloads, implement CI/CD, orchestration, testing, monitoring, observability, and infrastructure as code. Partner with data scientists on feature pipelines, data-quality investigations, model evaluation, and training-serving consistency. Develop interfaces for AI agent workflows and support reliable, scalable identity-resolution systems on AWS and Snowflake.

Develop and deploy production machine learning models for content classification and segmentation. Build multi-class and multi-label classifiers, perform exploratory analysis, feature engineering, model training, tuning, and evaluation, and create text- and metadata-based features using Python, SQL, pandas, and Spark. Develop scalable workflows, maintain tested and documented code, and collaborate with data scientists, engineers, and stakeholders.

Develops and deploys machine learning, deep learning, and visual AI models for defense and government applications. Responsibilities include data transformation, predictive modeling, anomaly detection, statistical analysis, neural network training, model reliability assessment, and AI/ML solution implementation. Leads technical projects and small teams while collaborating with stakeholders and external customers. The role requires expertise in Python-based ML frameworks, LLM prompting, Transformer architectures, and modeling and simulation tools.

Develop advanced personalization and recommendation systems for digital grocery shopping. Build, deploy, monitor, and maintain machine learning pipelines and microservice APIs supporting production inference. Apply natural language processing, generative AI, large language models, agentic AI, recommendation models, deep learning, transformers, and classical machine learning techniques to real-world business problems. Evaluate and tune models, communicate technical results, and contribute to scalable AI solutions.