Top Machine Learning Jobs

Develop and deploy production-grade AI and machine learning applications as part of a backend Python team. Responsibilities include integrating ML models and data pipelines, developing LLM prompts, supporting NLP, machine translation, and text-to-speech solutions, building CI/CD pipelines, implementing testing, and collaborating with Product and Engineering teams. The role requires strong Python and C++ skills, production deployment experience, and familiarity with ML frameworks and cloud-native tools.

Design, deploy, evaluate, and maintain recommendation, ranking, personalization, and predictive behavior models for Taboola’s advertising platform. Build ML evaluation harnesses, automated testing frameworks, benchmarks, and guardrails. Develop data pipelines and transformations using Kafka, SQL, HDFS, Hadoop, Spark, and distributed big-data systems while collaborating with product and technical teams.

Build, deploy, scale, and monitor production machine learning models and infrastructure for risk management. Responsibilities include model development, data pipeline construction, cloud and distributed systems operation, Kubernetes-based deployment, CI/CD, model governance, responsible AI, testing, monitoring, and collaboration with product and data science teams.

Design, deploy, and scale responsible AI and machine learning solutions supporting healthcare provider experiences. Lead AI initiatives, provide technical direction to AI engineers, evaluate emerging technologies, and partner with product, data, and business teams. Ensure models meet enterprise standards for performance, security, governance, and scalability while contributing to cloud modernization. The role emphasizes generative AI, LLMs, conversational AI, API development, cloud hosting, and production operations.

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.

Develops and deploys advanced machine learning, deep learning, computer vision, and AI models for defense and government missions. Responsibilities include data analysis, model architecture and training, predictive forecasting, anomaly detection, model assessment, and integration with simulation tools. Serves as a technical lead on complex initiatives, influences strategy, briefs senior leaders, and mentors technical staff. Requires expertise in transformers, LLM prompting, unsupervised learning, and deep neural networks.

Develop simulation technology for physical AI and robotics, integrating learning environments with engineering-grade simulators for dynamics, structures, fluids, and thermal applications. Build workflows combining simulation and AI-driven learning to accelerate robotics deployment. Preferred expertise includes machine learning, reinforcement learning, vision-language models, and vision-language-action models.

Develop and evaluate machine learning algorithms for satellite recognition, tracking, sensor scheduling, anomaly detection, prediction, classification, clustering, and optimization. Perform exploratory data analysis, build scalable HPC pipelines, conduct space-related engineering analyses, validate external algorithms, collaborate with satellite and astrodynamics experts, and present technical findings to customers and stakeholders.

Evaluate, validate, and improve AI/ML models and systems supporting a Department of Defense customer. Conduct rigorous performance, robustness, security, explainability, bias, and adversarial-resilience assessments; collaborate with R&D teams, researchers, engineers, industry partners, and government stakeholders; produce technical reports and recommendations; assess emerging technologies; and communicate findings throughout the development lifecycle.

Design and develop Voice AI applications with expertise in linguistics and speech. Own language and locale support, diagnose production speech failures, conduct linguistic experiments, evaluate STT/TTS systems, build analysis tooling, contribute code, collaborate with engineers and product managers, and mentor colleagues. The role requires advanced linguistics or related education, applied quantitative research experience, Voice AI development, programming skills, and knowledge of multiple languages.

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.

Build and operate production-grade agentic AI and large language model services for Finance workflows. Responsibilities include developing AWS cloud-native platforms, retrieval and context-engineering capabilities, APIs, front-end integrations, evaluation and observability systems, and scalable event-driven services. The role also involves optimizing performance and cost, implementing secure AI guardrails, troubleshooting distributed systems, and contributing to technical standards, documentation, code reviews, and responsible AI development practices.

Leads organization-wide software engineering initiatives for data pipelines, machine learning platforms, and scalable cloud infrastructure. Oversees architecture, technical execution, resiliency, cost optimization, model monitoring, data drift detection, and model explainability across multiple teams. Establishes engineering standards, governance, success metrics, and strategic technology direction while advising senior management and developing technical talent. The role requires extensive expertise in AWS, ML deployment, big data, infrastructure automation, APIs, agentic frameworks, and RAG.

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.

Own and advance production search, recommendation, retrieval, and ranking systems for a consumer social and interactive entertainment platform. Build candidate generation, embedding retrieval, two-tower models, ranking features, user and content representations, and online serving systems. Lead experimentation and evaluation to improve discovery, engagement, retention, personalization, cold-start performance, and content distribution while partnering with product, data, and engineering teams.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Optimize frontier model inference on TPUs across model code, kernels, and hardware. Responsibilities include profiling workloads, identifying bottlenecks, improving latency and throughput, developing optimized kernels, exploring quantization, building benchmarks, validating correctness, and collaborating with researchers and engineers to support new architectures and productionize performance improvements.

Build and own prediction and planning machine learning models for vehicle intelligence, including training, validation, dataset curation, deployment, and field testing. Develop scalable data ingestion and model improvement pipelines, identify software performance bottlenecks, and help shape the technical roadmap. The role involves hands-on work with prototype and customer vehicles and collaboration with company founders.

Design, build, and own a multi-tenant enterprise SaaS backend that feeds ML data and BI. Collaborate with product, data science, UX, and users to deliver scalable, reliable customer-facing AI solutions. Drive systems design, database schemas, development lifecycle standards, technical docs, and code reviews while using AI/agentic tools to accelerate development.

Leads the engineering team responsible for Nordstrom’s personalization engine, including recommendation models, training pipelines, real-time serving, and complete-look experiences. Owns technical direction, delivery, reliability, model and data quality, incident response, and team development. Partners with product, data science, platform engineering, and business stakeholders while remaining hands-on with architecture, code reviews, and AI-native development practices.

Develop and deploy advanced machine learning solutions for pricing, promotions, demand forecasting, and optimization. Own the end-to-end ML lifecycle from data exploration and model design through deployment, monitoring, and maintenance. Collaborate cross-functionally to improve revenue, profitability, and business efficiency using machine learning, statistical modeling, forecasting, generative AI, and production ML infrastructure.

Summer 2027 engineering interns will contribute to technically deep AI research and engineering across computer vision, embodied AI, reinforcement learning, robotics, agentic systems, language modeling, computational neuroscience, and ML engineering. Responsibilities include translating research code into scalable, reliable production systems and collaborating in a fast-moving research environment. Interns will work with a world-class team developing foundation models using large-scale neural data.

Design and build large-scale mapping algorithms and geospatial data systems for automated map reconstruction and maintenance. Develop lane, road-network, geometry, topology, and map-conflation systems; process sensor and third-party data through distributed pipelines; apply machine learning and computer vision for feature extraction and change detection; and create automated validation and quality systems. Partner with perception, localization, simulation, and platform teams while mentoring engineers and driving technical problems into production.