Top Machine Learning Jobs

Develops, integrates, tests, and deploys AI, machine learning, and autonomy algorithms for mission systems. Responsibilities include perception, planning, controls, human-machine interfaces, requirements translation, software design, debugging, verification, and fielded product integration. The role may also involve deep learning, generative AI, MLOps, robotics, cloud ML platforms, safety-critical testing, and mentoring engineers.

Develops production-grade AI and machine-learning solutions, including LLM integrations, predictive models, and end-to-end pipelines. Translates business needs into solution architectures, builds and fine-tunes models, performs integration testing, deploys through CI/CD, and monitors production systems. Collaborates with stakeholders, documents designs, supports user adoption, troubleshoots issues, and drives continuous improvement. The role also involves autonomy algorithms, perception, mission planning, controls, and human-machine interfaces.

Develops and validates predictive and deep-learning models through exploratory research and statistical analysis. Designs scalable GPU, distributed, and cloud computing solutions for large datasets; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes. The role uses machine learning, optimization, simulation, and potentially NLP or Generative AI to deliver business value.

Develops and validates predictive, deep-learning, and optimization models; performs exploratory statistical analysis; processes large datasets using GPU, distributed, and cloud computing; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes.

Develops and validates predictive and deep-learning models through exploratory research, statistical analysis, and large-scale data processing. Designs GPU, distributed, and cloud computing solutions; identifies patterns in complex datasets; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes. The role requires Python, TensorFlow, PyTorch, large-dataset experience, U.S. citizenship, and eligibility to obtain a Secret clearance.

Develops production-grade AI and machine-learning solutions, including LLM integrations, predictive models, and end-to-end pipelines. Translates business requirements into architectures, builds and fine-tunes models, integrates REST APIs, conducts testing, deploys through CI/CD, monitors solutions, documents designs, and drives continuous improvement. The role also supports autonomy software development, human-machine interfaces, algorithm integration, troubleshooting, and mission-system testing.

Develops, integrates, tests, and deploys AI, machine learning, and autonomy algorithms for mission systems. Translates requirements into software, supports perception, planning, controls, and human-machine interfaces, and troubleshoots deployed capabilities. Leads technical teams and guides AI/ML efforts from research or prototype stages through operational delivery, including technical strategy, integration, testing, risk management, and mentoring. Work may involve time-series, telemetry, sensor, anomaly detection, forecasting, and predictive modeling applications in aerospace or defense systems.

Develops, integrates, tests, and deploys autonomy, AI, and machine learning algorithms for mission systems. Leads technical and multidisciplinary engineering efforts, delivers production AI systems using LLMs and generative AI, and supports the full software lifecycle from requirements through implementation. Responsibilities include perception, planning, controls, human-machine interfaces, cloud deployment, troubleshooting, mentoring engineers, and guiding technical projects.

Develop and deliver AI/ML-enabled enterprise, cloud, and application software for national security space programs. Support software design, development, integration, testing, mission management, command and control, and data analytics. Collaborate with engineering teams on mission-focused capabilities using object-oriented programming, AI prompting, model training, evaluation, data preparation, Agile practices, unit testing, and configuration management.

Develop and deliver AI/ML, enterprise, cloud, and application-based software for national security space programs. Support software design, development, integration, testing, data analytics, mission management, and command-and-control capabilities. Collaborate with engineering teams, apply coding standards, perform unit testing, and use configuration management and Agile practices. The role requires familiarity with object-oriented programming, AI/ML concepts, Linux or UNIX, and software development lifecycle activities.

Owns the vision, roadmap, governance, lifecycle, reliability, and adoption of AI/ML technology platforms. Translates stakeholder needs into platform requirements, prioritizes capabilities, manages integrations and dependencies, partners with engineering and architecture teams, and drives continuous improvement. The role monitors performance, security, cost, and technical debt; supports agile delivery, incident escalations, stakeholder communications, and leadership reporting.

Build and lead production machine learning capabilities for Snapchat+ subscription growth and monetization. Responsibilities include developing personalization, ranking, propensity modeling, offer decisioning, retention, and lifecycle optimization systems; translating ambiguous business problems into ML solutions; conducting experimentation; deploying scalable services; and establishing technical direction. The role partners with Product, Data Science, Backend, and Mobile Engineering teams while measuring impact on revenue, subscriber value, and retention.

Develops data visualization, analytics, machine learning, and automation solutions for coherent optical product manufacturing. Mines wafer-to-module datasets to identify yield drivers, failure signatures, anomalies, correlations, and process drift. Builds and evaluates ML algorithms for yield prediction and root-cause analysis, performs statistical analysis, and automates engineering workflows using GenAI. Collaborates with R&D, manufacturing, and AI/ML teams to optimize product performance and communicate findings to technical audiences.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Leads AI/ML engineering teams developing, deploying, and scaling production machine-learning solutions. Establishes MLOps practices, automated CI/CD pipelines, model monitoring, and cloud-native architectures. Drives responsible AI innovation, evaluates emerging technologies, defines technical roadmaps, partners with stakeholders, and mentors engineering staff. The role focuses on operationalizing AI across the enterprise, including generative AI and advanced machine-learning solutions for healthcare challenges.

Leads the design, development, and deployment of AI and machine learning solutions for healthcare technology platforms. Builds scalable ML models, MLOps pipelines, LLM and RAG systems, backend services, and agent-based applications. Establishes standards for monitoring, testing, CI/CD, and lifecycle management while collaborating with engineering, data, product, security, and SRE teams. Provides architectural guidance and technical mentorship, evaluates emerging AI technologies, and promotes responsible, secure, and resilient AI implementation.

Develop production-ready machine learning and signal-processing algorithms for wearable physiological sensor data. Responsibilities include designing and evaluating deep learning models, analyzing large datasets, optimizing algorithms for embedded edge deployment, and balancing accuracy, power, memory, latency, and scalability. The role owns projects from research and prototyping through validation, deployment, monitoring, and continuous improvement while collaborating with scientists, engineers, hardware experts, and product teams.

Develop, evaluate, and integrate machine learning models for physical robots. Build data and evaluation workflows, run research experiments, analyze failures, and communicate tradeoffs. Integrate models into robot software, test them on real hardware, and optimize inference for practical latency and compute constraints. Collaborate with robotics and software engineers to turn research into reliable product capabilities.

Designs, develops, deploys, and operates scalable AI and machine learning solutions across the enterprise. Responsibilities include building end-to-end ML infrastructure, managing LLM lifecycles, optimizing RAG systems and prompts, evaluating models, deploying Azure-based solutions, writing tested software, supporting CI/CD and DevSecOps, monitoring production systems, and collaborating with engineering, architecture, DevOps, and testing teams.

Lead the architecture and production deployment of predictive ML and GenAI capabilities that improve clinical outcomes and member retention. Develop causal ML strategies, intervention-effectiveness models, reusable GenAI platform services, evaluation frameworks, safety guardrails, and cross-functional AI roadmaps. Partner with Product Engineering, ML Ops, Analytics, and clinical stakeholders while setting technical standards, mentoring engineers, and driving multi-quarter initiatives.

Software Engineering Intern developing AI-powered products and platforms involving machine learning, large language models, retrieval systems, inference services, evaluation frameworks, and scalable infrastructure. The role contributes to sign-language data, model training, evaluation, and product integration while collaborating with engineers, researchers, and linguistic experts. Interns apply software engineering principles, learn new methods, gather requirements, improve technical solutions, and enhance product reliability, observability, efficiency, and performance.

Build and operate production-grade data pipelines, forecasting models, financial-planning models, economic scenario workflows, and analytical products. Partner with modelers to operationalize statistical and machine learning solutions with testing, monitoring, controls, documentation, and scalable architectures. Establish technical direction across divisions, manage stakeholders, review designs and code, mentor practitioners, and support Finance, Risk, Engineering, and business teams.

Develops, deploys, monitors, and supports production machine learning solutions within collaborative product teams. Responsibilities include algorithm and software design, data engineering, infrastructure automation, testing, dashboards, logging, alerting, performance and capacity monitoring, and production support. The engineer collaborates with product, UX, engineering, and business teams, reviews code, maintains quality standards, and contributes to continuous learning and enablement.

Leads AI and machine learning data engineering development projects, including global initiatives. Manages resources and development teams, improves quality and engineering practices, provides subject matter expertise, develops business plans, mitigates risks, handles escalations, and drives technology innovation and change management.

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Develop reusable infrastructure and services, integrate research innovations into reliable products, drive architecture, and solve scalability, performance, reliability, security, privacy, and Responsible AI challenges. Provide hands-on technical leadership and mentorship across complex engineering initiatives.