Top Senior Machine Learning Jobs

Lead design and delivery of end-to-end ML solutions for Wealth Management, build and deploy scalable models (recommender systems, GNNs, LLMs), run large experiments, validate and monitor production models, partner with cross-functional teams, and mentor data scientists to drive measurable business outcomes.

Develop novel AI approaches for healthcare using structured and unstructured data, build ML models, conduct experiments, and collaborate with stakeholders.

Develop machine learning and generative AI capabilities for conversational enterprise search, including multi-turn question answering, information retrieval, ranking, evaluation, and LLM-enabled features. Build scalable platform frameworks that help product teams prototype and deploy search applications. Establish metrics for search quality, security, privacy, and scalability while collaborating with product, design, infrastructure, data science, and engineering teams. Contribute to a high-performing and inclusive engineering culture.

Develop and productize deep learning perception systems for autonomous driving, including traffic signal, sign, road marking, and construction object understanding. Lead end-to-end model development, data-driven improvement, simulation, augmentation, and deployment to embedded platforms. Optimize solutions for accuracy, safety, latency, robustness, and regional generalization while collaborating with data-labeling teams and contributing technical leadership.

Build and operate production small language models for real-time AI governance. Responsibilities include model training, fine-tuning, distillation, high-performance inference, synthetic-data generation, evaluation, telemetry, adversarial testing, failure diagnosis, and closed-loop model improvement. The role also provides technical leadership and partners with product, security, research, and customer-facing teams to deploy reliable, low-latency models at enterprise scale.

Leads Tala’s Machine Learning Engineering team in building and operating platforms for model development, real-time inference, streaming features, batch processing, deployment, monitoring, and production reliability. Manages and develops 4–6 engineers, owns delivery and prioritization, provides architectural leadership, improves engineering practices and SLOs, and partners with Data Science, Data Engineering, Product, Credit, and Business Development teams.

Develop and productionize machine learning models for supply chain forecasting and optimization. Responsibilities include creating model architectures, curating proprietary real-world data, integrating LLM reasoning and tool use, designing evaluations, and continuously improving performance as market conditions change. The role owns the full research cycle from hypothesis through production and requires collaboration in a fast-moving, in-person environment.

Designs, trains, evaluates, and deploys AI/ML and deep learning models for mission-critical applications. Analyzes complex datasets, develops predictive algorithms, integrates models into operational systems, and supports testing, validation, deployment, and lifecycle management. Collaborates with engineers, data scientists, and mission stakeholders; documents AI/ML initiatives; researches emerging technologies; and may support LLM, generative AI, NLP, cloud, distributed computing, and MLOps capabilities.

Leads the architecture and production delivery of a novel AI-powered exploitability analysis engine. Owns probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drives zero-to-one development, technical direction, architecture and code reviews, cross-functional partnerships, and mentorship. Requires deep expertise in production AI/ML, LLMs, distributed systems, cloud-native development, probabilistic modeling, and scalable architectures, with strong security experience preferred.

Own the architecture and production delivery of a novel AI-powered exploitability engine. Lead probabilistic ranking, graph modeling, entity resolution, calibration, validation, and security guardrails for agentic systems. Drive zero-to-one technical direction, architecture, code reviews, cross-functional partnerships, and mentorship. Build scalable systems using distributed technologies, cloud-native development, APIs, databases, and machine learning while establishing defensible engineering decisions and evaluation metrics.

Own and deliver a major subsystem of ServiceNow’s exploitability analysis engine, such as probabilistic scoring, exposure graphs, entity resolution, or calibration. Lead architecture, design and code reviews, production delivery, metrics, AI safety guardrails, and technical direction. Partner with product, security R&D, and SecOps while mentoring engineers and guiding ambiguous AI/ML problems to scalable, reliable production systems.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Perform data analysis and test & evaluation of machine learning algorithms; develop algorithms for physical and autonomous systems; transition algorithms to software and embedded systems; work with stakeholders to present results and solutions; research new ML and data-analysis methods; handle large, multidimensional datasets and data pipelines.

Design, develop, and evaluate large-scale multimodal foundation models for biological research. Build hybrid LLM and graph neural network architectures, relational foundation models, and distributed multi-GPU training systems. Apply deep learning theory to biological data, transition research prototypes into scalable production systems, publish peer-reviewed research, and mentor junior staff while collaborating across scientific and engineering teams.

Build and operate Unity’s machine learning infrastructure, including data and feature pipelines, distributed training workflows, orchestration, model serving, monitoring, validation, and testing. The role involves delivering ML platform projects end-to-end, troubleshooting complex pipeline issues, improving reliability and reproducibility, and collaborating with ML engineers, researchers, managers, and partner teams.

Leads the design, development, and deployment of AI-powered solutions, intelligent automation, and digital workers. Prototypes and scales generative and agentic AI capabilities, integrates them with enterprise platforms and workflows, and establishes AI engineering, governance, security, privacy, and responsible AI standards. The role also leads technical workstreams, mentors engineers, evaluates emerging technologies, and drives enterprise adoption of innovative AI solutions.

Design, develop, test, and productize AI/ML algorithms. Responsibilities include selecting and analyzing data, training and improving models, evaluating system performance, integrating scalable solutions, validating data and model quality, and identifying distribution issues. The role requires software engineering expertise, machine learning deployment experience, and oversight of iterative verification and validation processes.

Build and deploy production machine learning systems for POLITICO’s newsroom and products. Develop classification, prediction, ranking, NLP, and model adaptation solutions; create data pipelines, evaluation frameworks, and feature engineering systems; and collaborate with engineers, journalists, editors, and product teams to integrate reliable, cost-effective AI capabilities into production.

Build and deploy scalable AI and machine learning products for clinical research, including patient recruitment, enrollment forecasting, and study feasibility. Develop ML models, LLM-powered agents, data pipelines, and MLOps infrastructure using cloud services and CI/CD automation. Collaborate with cross-functional teams to integrate AI into clinical research workflows, monitor model performance, and advance reliable, unbiased solutions that improve patient outcomes.

Leads the design, development, training, deployment, and optimization of scalable machine learning models, pipelines, and production systems. Provides technical guidance to engineers and data scientists, reviews ML code, contributes to platform architecture, and drives end-to-end workflows from data ingestion through model serving. Collaborates with data science, data engineering, and product teams while troubleshooting models and resolving technical issues.

Develop and deploy production-ready machine learning models for game bots, intelligent NPCs, companions, world modeling, moderation, security, and QA automation. The role focuses on deep learning, computer vision, natural language processing, foundation models, reinforcement learning agents, reward design, policy optimization, data development, model evaluation, and scalable training environments. Candidates will collaborate with developers using agile practices and contribute to the roadmap for applied machine learning systems in gaming.

Design and implement production-grade machine learning systems for counterfeit detection, commerce risk, logistics, fulfillment, and post-purchase applications. Translate business problems into ML solutions, develop scalable pipelines, define performance metrics, and own ML roadmaps. Collaborate with product, operations, and backend engineering teams while mentoring technical talent and driving innovation across complex global initiatives.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Build and deploy video recommendation models and APIs, research LLM and computer vision applications, and develop semi-automatic annotation techniques for NLU models. Retrain and evaluate machine learning systems, improve Roku Voice data annotation pipelines, collaborate with engineering and product teams to launch research into products, and document technical work through design documents, demos, and presentations.

Develop and deploy machine learning systems powering AI agents for permitting workflows. Responsibilities include building LLM and NLP solutions, RAG and vector search systems, document-processing pipelines, scalable ML infrastructure, and production backend services. The role owns the full ML lifecycle, including experimentation, deployment, monitoring, evaluation, and iteration, while collaborating with engineering, product, and construction-domain experts.