Top Senior Machine Learning Jobs

As a Senior AI Engineer, you'll develop and deploy innovative AI solutions for the fashion industry, collaborate with cross-functional teams, and refine machine learning models to enhance product offerings.

Lead development and operationalization of pricing algorithms to value vehicles, deliver $15-30M/year in incremental value, build and manage a 4-8 person data science team, develop AI/ML models to autonomously price vehicles, run experiments, and implement model monitoring and incident response with cross-functional partners.

Lead and mentor a team of machine learning engineers, drive ML projects, and collaborate across departments to enhance product features and model improvements.

Develop and optimize predictive models for credit and risk, leveraging advanced machine learning techniques to improve accuracy and decision quality.

Develop and deploy production-grade machine learning models, manage model lifecycles through MLOps practices, and support ML platforms and operations. The role requires strong Python, PyTorch, TensorFlow, Azure, software engineering, and coding expertise. Preferred experience includes generative AI, large language models, retrieval-augmented generation, vector databases, and semiconductor or manufacturing domains.

Set the technical direction for machine learning across underwriting, fraud, risk, and personalization. Own the ML platform and modeling roadmap, including feature stores, pipelines, serving, monitoring, governance, and experimentation. Lead high-impact credit and cash-advance models through deployment and iteration, establish model-risk standards, partner with business and compliance leaders, and mentor engineers. This is a hands-on staff role with company-wide technical influence.

Develop deep learning models using imitation and reinforcement learning to generate autonomous vehicle trajectories. Build production ML pipelines, metrics, and error-analysis tools; evaluate driving plans for safety, progress, and comfort; and collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving in complex urban environments.

Develop scalable AI software by translating Python research code into performant Rust components. Build extensive automated tests, runtime services, simulations, interfaces, and deployment tooling. Profile and optimize CPU, memory, latency, and throughput while supporting concurrency, interoperability, containerization, observability, cloud, on-premises, and edge environments. Collaborate with researchers and engineers on algorithms, documentation, production validation, and confidential intellectual property.

Architect and build production-grade AI and machine learning systems by translating Python research implementations into scalable Rust components. Lead decisions involving concurrency, parallelism, memory, distributed processing, testing, profiling, observability, deployment, and performance. Collaborate with researchers, mentor developers, protect intellectual property, and support cloud, on-premises, embedded, and edge environments.

Design, build, deploy, and operate production AI/ML systems for generative AI, search, retrieval, ranking, recommendations, personalization, and intelligent reader experiences. Translate research prototypes into scalable, low-latency services; develop ML platforms, feature pipelines, model serving, inference, observability, and optimization systems. Provide technical leadership through architecture reviews, mentoring, and roadmap development.

Leads architecture, implementation, and scaling of production ML and LLM systems for legal products. Builds RAG pipelines, agentic applications, hybrid search, APIs, microservices, data pipelines, and model-serving infrastructure. Establishes reusable AI frameworks, deployment and observability practices, and CI/CD standards while optimizing latency, reliability, scalability, and cost. Partners with data scientists to productionize models and provides technical leadership in system design and engineering excellence.

Set the technical direction for machine learning at Payabli by maturing production risk models and launching new models across the payments lifecycle. Establish experimentation, monitoring, drift detection, benchmarking, retraining, and incident-response foundations. Translate payments challenges into modeling opportunities, connect model performance to business outcomes, influence the ML roadmap, and mentor engineers while partnering with product, engineering, and risk operations.

Work with enterprise customers to identify high-value tabular ML use cases, design production-grade architectures, prototype solutions, lead deployments, coordinate cross-functional teams, and codify reusable deployment patterns to scale adoption of the NEXUS tabular foundation model.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

Lead improvements to behavior and trajectory prediction for autonomous vehicles. Own prediction work within Planning, collaborate with ML, Perception, Simulation, and Systems teams, refine prediction-planning interfaces, define metrics and evaluation frameworks, analyze failures via logs/simulation, and lead technical projects while mentoring junior engineers.

The Senior Principal, ML/AI will lead cross-functional teams in developing and implementing ML/AI solutions, driving significant business impact through innovative technical leadership over a defined roadmap.

Lead cross-functional ML/AI initiatives, drive technical roadmaps, mentor teams, and deliver impactful ML/AI solutions for business growth.

Lead the strategy, architecture, and production delivery of large-scale machine learning systems for recommendations, matching, ranking, retrieval, and interaction prediction. Own the full model lifecycle from research and feature engineering through distributed training, experimentation, evaluation, and latency optimization. Mentor machine learning engineers and data scientists, establish organizational best practices, and partner with engineering and product leadership on the ML roadmap.

Lead design, development, deployment, and maintenance of large-scale ML systems and pipelines. Provide senior technical consulting, resolve complex ML and governance issues, plan end-to-end model integrations, and document implementations for performance optimization.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.

Build and productionize applied AI and machine learning capabilities for Crucible, including LLM workflows, retrieval, tool use, orchestration, evaluation, and model-serving systems. Integrate optimization and forecasting models into reliable products, select models for constrained cloud, air-gapped, and edge environments, and establish technical standards for production AI. Collaborate with manufacturing, supply chain, data science, software engineering, and product teams to deliver measurable operational value.

Lead design, training, and evaluation of multi-modal ML perception models (camera, lidar, radar) for fallback autonomy. Define metrics, curate datasets, implement training and optimized inference pipelines for on‑vehicle constraints, and integrate models into production. Partner with safety, systems, and product teams for validation, verification, and deployment. Mentor engineers and promote ML and software engineering best practices.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions, including LLM applications, multi-agent systems, agentic workflows, and enterprise automation. Own the AI solution lifecycle in cloud environments, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders to deliver enterprise-scale solutions.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM applications, multi-agent systems, agentic workflows, RAG solutions, and automation using cloud AI platforms. Own the full AI solution lifecycle, influence architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.

Design, develop, deploy, and maintain production-grade AI and machine learning solutions for enterprise corporate functions. Build LLM orchestration, RAG applications, agentic workflows, multi-agent systems, and automation using cloud AI platforms. Own the full AI solution lifecycle, establish architecture and engineering standards, apply MLOps and responsible AI practices, and collaborate with data, analytics, and business stakeholders.