Top Machine Learning Jobs

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.

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.

Graduate AI/ML engineering intern developing intelligent tools, predictive models, workflows, and prototypes for footwear and apparel design, simulation, and product innovation. Responsibilities include training models with simulated and experimental data, applying AI/ML to engineering workflows, building interactive internal tools, evaluating outputs, presenting findings, and documenting work for broader adoption.

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.

Develop and productionize machine learning models for industrial process optimization using spectral and multimodal sensor data. Responsibilities include designing experiments, building preprocessing and feature extraction pipelines, monitoring model and sensor drift, detecting anomalies, developing ML infrastructure, and applying chemometrics, hybrid modeling, self-supervised learning, probabilistic reasoning, and time-series techniques.

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.

Design, develop, test, evaluate, deploy, and maintain AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Process structured and unstructured data, build ETL pipelines, conduct experiments, measure performance, deploy models in cloud environments, document findings, brief stakeholders, educate staff, and provide AI/ML subject matter expertise.

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.

Build and operate production machine learning systems for recommendations, ranking, personalization, contextual bandits, and agent-based recommendations. Responsibilities include model development, feature and data pipelines, evaluation, deployment, monitoring, experimentation, and reliability improvements. The role partners with engineering, data science, product, marketing, and sales teams to translate business problems into measurable ML outcomes.

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.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

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.

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 productionize end-to-end machine learning models, automated bidding and allocation algorithms, and data pipelines to optimize performance marketing spend. Manage ad platform integrations and bidding automation, automate creative/design workflows, solve loosely defined optimization problems, and collaborate cross-functionally with marketing, product, and leadership.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Own robotic grasping intelligence by designing bespoke neural networks, building cloud training and evaluation pipelines, deploying models to robot fleets, and improving performance through field telemetry. Develop simulation-first manipulation strategies using robotics concepts including motion planning, kinematics, geometry, and perception. Collaborate with perception and motion-planning engineers while setting technical direction for a small applied ML team.

Develop detection, classification, image analysis, and AI/ML algorithms for security screening systems. Build data analysis, testing, validation, and model improvement tools; research emerging machine learning and deep learning methods; and collaborate with engineering teams to transition concepts from research into production. The role also requires technical documentation, stakeholder communication, and contributions to aviation and homeland security technologies.

Build and optimize distributed machine learning infrastructure for large-scale model training and inference. Responsibilities include profiling training runs, improving compute, communication, and storage efficiency, developing parallelism and quantization strategies, creating CUDA and Triton kernels, and ensuring fault tolerance, checkpointing, deterministic orchestration, and reliability across GPU clusters. The role partners closely with research scientists to scale new model architectures and training recipes.