Top Machine Learning Jobs

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.

Conduct R&D on AI/ML and machine translation for SIGINT and cybersecurity applications. Build research prototypes, evaluate and enhance MT tools and text-processing pipelines, and implement containerized solutions. Resolve complex AI technical problems and collaborate with senior technical staff.

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.

Join Ello as an AI/ML Engineer to create AI-driven educational products, enhance learning experiences, and analyze educational outcomes through machine learning.

Join Trexquant as a Global Alpha Researcher to develop trading signals, implement research, and apply machine learning for alpha discovery.

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, develop, deploy, and improve real-time machine learning systems for Lyft’s Fulfillment team. Build feature pipelines, training workflows, and model-serving infrastructure; translate research into production; evaluate models against business KPIs; run experiments; write production code; conduct code reviews; and collaborate with product, data science, and engineering teams.

Leads development of BEV and multi-modal perception models for autonomous trucks, combining camera, LiDAR, radar, and HD map data. Responsibilities include model architecture, large-scale distributed training, data pipelines, robustness evaluation, sensor fusion, research into foundation models, and mentoring ML engineers. The role requires deep expertise in 3D vision, autonomous systems, multi-view fusion, and production-scale machine learning.

Build and deploy computer vision and machine learning solutions from research prototypes into scalable production services. Develop MLOps pipelines for data preparation, training, validation, versioning, deployment, and monitoring. Train and optimize deep learning models, integrate them into secure cloud systems, and support national security missions. Collaborate with mission partners, engineers, and domain experts while applying containerization, DevSecOps, model governance, and inference optimization practices.

Designs, builds, and evaluates language models for AI security products. Responsibilities include dataset curation, preprocessing, LLM experimentation, safety and robustness evaluation, dashboards, knowledge graphs, model fine-tuning, production integration, monitoring, CI/CD, and ML tooling. Collaborates with researchers and data scientists to automate quality assurance and guardrails while delivering reliable, scalable, and interpretable models.

Apply mathematics, statistics, and computer science to design and implement ML and advanced analytic methods for large, messy NSA/CSS datasets. Curate and model data, perform statistical and numerical analyses, translate mission needs into technical requirements, and clearly communicate findings to non-technical audiences. Maintain awareness of collection, processing, and analytic capabilities to recommend solutions.

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.

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.

Develop generative and predictive machine learning models for therapeutic antibody sequence, structure, and property design. Build multi-objective optimization and agentic LLM methods for lab-in-the-loop protein engineering, deploy models with engineering teams, and support drug development programs. Collaborate with drug developers, wet-lab scientists, automation specialists, and data scientists to translate ML methods into validated antibody candidates.

Build and maintain production AI/ML applications, backend services, data pipelines, and machine learning models. Develop agentic AI systems, LLM and RAG solutions, and MLOps capabilities including monitoring, versioning, A/B testing, and retraining. Collaborate with product, software, platform, and data teams to deliver reliable, explainable, and performant AI solutions for financial markets.

Develop and apply ML and statistical methods for experimental data, maintain and document analysis methods, collaborate with engineers to transition algorithms to prototypes, support field integration and testing, and conduct theoretical and experimental evaluation. Travel up to 15% for deployments and data collection.

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.