Top Senior Machine Learning Jobs

Develops AI/ML algorithms and ETL infrastructure supporting LLM, NLP, predictive analytics, and other customer solutions. Builds and maintains data pipelines, curates evaluation data, improves scalability and automation, and collaborates with researchers and developers using Agile methods. The role requires Python-based data engineering, machine learning experience, and an active TS/SCI clearance, with preferred experience in cloud, containers, distributed computing, reinforcement learning, GenAI operations, and GPU programming.

Designs, develops, deploys, and integrates production-grade AI/ML systems for national security missions. Responsibilities include building NLP, LLM, computer vision, and deep learning models; developing scalable data pipelines, APIs, microservices, and cloud or edge services; optimizing and evaluating models; and applying MLOps, security, reliability, and software engineering practices. The role collaborates with mission experts and engineering teams and requires an active TS/SCI clearance.

Build and deploy production machine learning systems across computer vision, multimodal modeling, structured data, and agentic AI. Own projects from problem definition through monitoring, develop data and evaluation pipelines, establish feedback loops, and translate experiments into practical technical recommendations. Collaborate with product, engineering, operations, annotation teams, and domain experts while delivering maintainable code, documentation, and scalable ML solutions.

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.

Own and advance a production physician-organization matching engine from rules and weights to semantic matching, embeddings, and vector search. Build rigorous precision/recall evaluation, entity resolution across 8M+ NPI records, and a reliable lightweight data platform using healthcare data. Independently own ingestion, enrichment, modeling, serving, and architecture decisions while improving recommendation quality and supporting customer-facing reporting.

Serve as a technical partner to sales and customer success teams, advising high-value customers on cloud infrastructure and AI/ML solutions. Responsibilities include architecture reviews, proofs of concept, demos, onboarding, technical workshops, infrastructure optimization, LLM deployment and fine-tuning, GPU workload optimization, AI application design, data pipeline and database integration, escalations, documentation, and development of tools for premium customers. Collaborate globally with Engineering, Product, Support, and account teams.

Build, train, deploy, and maintain large-scale machine learning models and AI agents for marketing and customer experiences. Develop conversational AI, generative AI, recommendation, retrieval, ranking, and transformer-based systems. Own ML product lifecycles, including success metrics, A/B testing, production operations, and iterative improvement. Collaborate with Engineering, Product, Design, customers, and stakeholders while leading technical projects and mentoring teammates.

Build production-grade AI and agentic systems for healthcare workflows. Responsibilities include designing data pipelines, evaluation harnesses, model deployment and monitoring systems, and scalable LLM- and vision-based applications. The role spans backend and full-stack development, AI agent orchestration, experimentation, training pipelines, and frontend integration while collaborating with ML researchers and engineering teams.

Develop and productionize personalized search ranking, recommendation, and natural-language discovery systems. Apply deep learning, transformer models, graph neural networks, multimodal signals, LLMs, and behavioral data to improve search relevance. Partner cross-functionally on experimentation, model development, agent evaluation, and MLOps. Provide technical guidance through code reviews and best-practice sharing while advancing scalable machine-learning systems for e-commerce discovery.

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.

Develop AI/ML-powered control systems, digital twins, simulation environments, and machine learning models for data center cooling equipment. Build reduced-order models, maintain FMU packages, investigate reinforcement learning and model-predictive control strategies, and deploy inference pipelines on Linux platforms. Integrate equipment and simulations using industrial protocols including MQTT, Modbus, and CAN while collaborating with thermal, mechanical, and controls engineers.

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, build, and productionize scalable machine learning systems for personalized health and fitness metrics using physiological and behavioral data. Responsibilities include improving ML pipelines, translating research prototypes into reliable production services, developing operational infrastructure, implementing observability and incident response, and mentoring engineers and applied scientists.

Develop and implement AI/ML algorithms across computer vision, NLP, time series, reinforcement learning, graph data, and large language models. Analyze multimodal datasets, apply research findings, support training and deployment pipelines using AWS, create technical documentation, and mentor team members. The role requires Python and deep learning expertise, version control experience, and the ability to obtain a TS/SCI clearance.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Responsible for validating payment network models, including fraud and AML. Engage with stakeholders, improve model accuracy, and leverage innovative technology for data analysis.

Lead and grow a research engineering team developing conversational AI agents. Own the technical roadmap across model post-training, evaluation, safety, speech, and agent behavior; convert research ideas into measurable production programs; review architecture and experiments; ensure rigorous, reproducible engineering; and partner with infrastructure, platform, product, and customer teams to deploy reliable AI systems.

Build and validate statistical and machine learning models for credit card loss forecasting, modernization, and allowance (CCAR). Partner with data scientists, engineers, and product managers to transform data, automate insights, support model governance, and present model risk impacts to executives using cloud and big-data tools.

Own machine learning at Metriport end to end, including problem framing, feature engineering, model training, evaluation, deployment, monitoring, and infrastructure. Build predictive models and document-processing systems from large, messy clinical datasets. Develop training and inference pipelines, experiment tracking, model versioning, and production monitoring. Collaborate with founders, engineers, and customers to identify high-value ML opportunities and deliver reliable healthcare intelligence.

Build and maintain machine learning training, evaluation, data access, and model infrastructure for biological research. Responsibilities include designing large-scale data storage and versioning systems, maintaining a model zoo, supporting lab-in-the-loop training, making build-versus-buy decisions, and collaborating with AI scientists to develop novel models.

Technical lead for end-to-end ML and CV pipelines to reconstruct, validate, and maintain map primitives from multi-modal vehicle sensor data. Architect scalable offline and production map reconstruction systems, define technical strategy, drive cross-functional integration, mentor senior engineers, and ensure robustness, safety, and operational scalability for national-scale mapping.

Lead development of large-scale, low-latency ML systems for ad serving to optimize pricing, allocation, and auctions. Design experiments, build production ML pipelines and model serving, partner cross-functionally, and provide technical leadership and mentorship.