Top Machine Learning Jobs

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.

Lead end-to-end decision science engagements for Federal clients using mathematical optimization, simulation, machine learning, statistical modeling, and analytics. Design, develop, deploy, and maintain production models; build agentic workflows; write Python; mentor junior scientists; manage projects; communicate complex findings to stakeholders; support proposals and business development; and contribute to internal practice development.

Lead the AI & Analytics stream to design, build, and deploy predictive and prescriptive ML models, integrate analytics into enterprise platforms (Salesforce, ServiceNow), automate root-cause and log analysis, translate stakeholder needs into production solutions, and manage analytics projects end-to-end while communicating results to technical and executive partners.

Build and ship AI/ML products for finance and accounting workflows. Responsibilities include transforming messy financial data into production systems, designing evaluations, analyzing errors, optimizing quality and cost, and partnering with product and engineering. The role requires practical experience with machine learning, LLM applications, retrieval, embeddings, agentic systems, noisy data, and end-to-end production deployment.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Own end-to-end AI data verticals, building scalable machine learning and backend distributed systems while driving product strategy, revenue, customer growth, and market expansion. Partner with strategic customers, solve complex production ML challenges, and lead applied AI initiatives involving evaluation, physical AI, multimodal systems, and LLMs. The role requires founder-minded technical and business ownership in an ambiguous, high-growth environment.

Build, deploy, and maintain production machine learning models and infrastructure. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software engineering practices. The role partners with Product and Data Science teams in an Agile environment and uses technologies including Python, PyTorch, TensorFlow, Spark, Kubernetes, and major cloud platforms.

Design, build, deploy, and operate production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, and distributed ML systems; automate testing and deployment; monitor and retrain models; and apply responsible AI, governance, and explainability practices. Collaborate with Product, Data Science, and Agile engineering teams to solve complex business problems at scale.

Build, deploy, optimize, and monitor machine learning models and infrastructure at scale. Develop production ML applications, data pipelines, cloud architectures, automated testing, and CI/CD workflows. Collaborate with product and data science teams, operate distributed systems, maintain models in production, and apply responsible AI, model governance, security, and explainability practices.

Build, deploy, and maintain production machine learning models and infrastructure. Design ML components, optimize algorithms, create data pipelines, operate distributed systems, and manage cloud-based containerized services. Apply testing, CI/CD, monitoring, model governance, responsible AI, and explainability practices while collaborating with product, data science, and cross-functional engineering teams.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring models, applying responsible AI practices, and collaborating with product, data science, and engineering teams. The role requires advanced programming, machine learning, distributed computing, Kubernetes, and cloud experience.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.

Build, deploy, scale, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production services, and applying responsible AI and governance practices. The role collaborates with product, data science, and Agile engineering teams to solve complex business problems.

Design, build, deploy, retrain, and monitor production machine learning models and components. Develop scalable ML infrastructure, data pipelines, cloud architectures, and distributed systems using Python and related technologies. Collaborate with Product and Data Science teams, automate testing and deployment, apply CI/CD practices, and ensure model governance, security, responsible AI, and explainability across enterprise platforms.

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.

Develop machine-learning surrogate models and optimization workflows for Formula 1 ERS design. Build neural networks emulating thermal, electrical, and degradation behavior; process large-scale telemetry and simulation datasets; implement genetic algorithm and Bayesian optimization loops; perform sensitivity and uncertainty analysis; integrate ML workflows with engineering simulation tools; and validate performance against real duty cycles under FIA constraints.

Design, build, and optimize personalized recommendation systems for Fortnite Discover, including candidate generation, ranking, reranking, user representations, and impression allocation. Develop deep learning retrieval models, run A/B experiments, establish evaluation frameworks, and deploy models to production. Collaborate on content quality and ranking signals while making infrastructure decisions across batch, near-real-time, and streaming architectures.

Design, build, and optimize personalized recommendation systems for Fortnite’s Discover experience. Develop retrieval, ranking, reranking, user representation, and candidate-generation models using deep learning. Lead A/B experimentation, evaluation frameworks, production deployment, and ML infrastructure decisions across batch, near-real-time, and streaming systems. Collaborate on content quality and ranking signals while balancing relevance, diversity, fair exposure, cold-start challenges, and creator incentives.

Provides senior systems engineering leadership for integrating AI/ML capabilities into MQ-9 unmanned aviation systems. Develops requirements, architectures, implementation strategies, roadmaps, trade studies, technical evaluations, and engineering analyses. Oversees AI/ML data management, model validation, integration, testing, and deployment while coordinating government, industry, and engineering stakeholders. Supports defense acquisition, fielding, sustainment, deficiency investigations, and executive-level technical decision-making within the NAVAIR program environment.

Develop and implement AI/ML algorithms across computer vision, natural language processing, time series, graph data, reinforcement learning, and large language models. Analyze large datasets, apply research publications, document technical solutions, and understand AWS-based training and deployment pipelines. Mentor team members and collaborate within interdisciplinary research teams.

Build and own a new AI product and its production infrastructure. Responsibilities include designing validated AI agent systems, secure enterprise software connectors, developer tooling, code analysis capabilities, low-latency model serving, monitoring, retrieval and language-model pipelines, and model tuning. The engineer will work directly with customers, ship production code, establish engineering standards, and influence team culture and product direction.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Deploy machine learning algorithms and conduct AI/ML model testing and evaluation. Apply software engineering expertise and advanced knowledge of multiple programming languages to support high-performance computing, large-scale data analytics, and cutting-edge information technology solutions. An active TS/SCI clearance with polygraph is required.

Apply machine learning techniques to high-performance computing, large-scale data analytics, and advanced information technology programs. Develop solutions using established machine learning frameworks such as PyTorch and TensorFlow. The role requires an advanced degree in artificial intelligence, data science, computer science, deep learning, or advanced mathematics, with substantial applied machine learning experience.

Build, deploy, optimize, and monitor machine learning models and infrastructure at scale. Responsibilities include developing ML applications, data pipelines, cloud-based architectures, CI/CD automation, production model operations, distributed systems, testing, monitoring, governance, and responsible AI practices. The role requires collaboration with product and data science teams and expertise in modern ML frameworks, algorithms, and containerized cloud environments.