Top Senior Machine Learning Jobs

Design, prototype, and productionize AI systems (recommendations, natural language interfaces, agents). Partner with product and engineering to identify AI opportunities, build scalable ML infrastructure, and manage the full ML lifecycle from data curation through training, deployment, and monitoring.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.

The Machine Learning Engineer will design, develop, and implement advanced recommendation systems, collaborating with cross-functional teams to enhance personalization for a global audience.

Lead end-to-end supply chain planning science: own forecasting tournaments, inventory placement optimization, vendor performance systems, and raw-material signals. Define methodology, production models, and iteration loops; drive AI-native workflows (LLM-aided EDA, agentic feature engineering), partner with planning operators and engineering, and mentor junior scientists to raise methodological standards and deliver measurable business impact.

The role involves designing and deploying machine learning systems for content safety at Spotify, collaborating with various stakeholders, and providing technical leadership.

Lead ML Engineer for Finance Tech to design, build, deploy, and maintain AI/ML systems (including LLMs and RAG). Partner cross-functionally to create production data pipelines, model evaluation, governance, observability, and responsible AI practices while contributing technical vision and scaling AI infrastructure.

The Senior Staff Machine Learning Engineer will lead innovative AI engineering projects and optimize algorithms for e-commerce applications, collaborating across teams to drive significant business impact.

Lead performance measurement and release management for the core autonomy stack across passenger, trucking, and mining programs. Define reliability metrics and forecasts, coordinate end-to-end releases to downstream teams, prioritize engineering projects to improve driving performance, and build reporting and dashboards for engineering and customer-facing teams.

Lead development and production deployment of advanced ML/AI pricing models across Home, Renters, Car, and Pet lines. Own end-to-end projects, align stakeholders, mentor junior data scientists, set technical standards, and drive adoption of advanced data science practices within Insurance.

Lead architecture and implementation of ML-driven features (including generative AI/LLMs and optimization) across cloud and mobile. Build production model deployment, CI/CD and experimentation infrastructure, define technical strategy from ambiguous product requirements, and own Agile delivery and technical risk communication while collaborating with Product, Engineering, and Data Science.

Design, build, and deploy machine learning models to detect fraud, prevent losses, and monitor risk. Work with Product, Engineering, and Operations to turn large-scale data into production-ready pipelines and model-serving solutions, monitor performance, and continuously improve model accuracy and business impact.

Lead integration of learned models into the autonomous behavior planning stack. Collaborate with ML and autonomy teams to design, validate, monitor, and deploy learned planning components while ensuring safety, fallback strategies, debuggability, and production readiness. Mentor engineers and drive cross-functional technical designs using simulation, logs, and real-world data.

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.