Top Machine Learning Jobs

Build next-generation voice AI systems for consumer and business applications. Develop persistent memory, complex reasoning, speech pipelines, and production-grade agentic workflows. As an early technical contributor, the ML Engineer will help shape core architecture, take ambiguous concepts through production, and influence user interactions with advanced language models in a fast-paced pre-seed startup.

Lead the technical direction, architecture, and delivery of large-scale machine learning systems for autonomous vehicle data. Develop autonomy algorithms, foundation models, deep scene understanding, causal models, high-quality datasets, and auto-labeling systems. Mentor senior engineers, define ML platform requirements, lead complex projects from conception through production, and coordinate integration across Uber engineering teams.

Develop and optimize machine learning models for Waymo’s autonomous driving perception systems. Responsibilities include implementing LoRA and QLoRA fine-tuning in JAX/Flax, building knowledge-distillation pipelines, validating quantization and model optimization techniques, benchmarking latency, and evaluating perception performance across large autonomous-driving datasets. The intern will collaborate with model optimization, hardware, and latency teams while working on multi-task vision transformers and onboard deployment.

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

Paid 6–12 month research internship on Epic's Special Projects & Research team. Build and evaluate ML models across vision, language, and audio to support player safety, game agents, world and content generation, and developer tools. Own data analysis and dataset creation, implement end-to-end models, run experiments, and collaborate with research scientists and engineers on deploying models at scale.

Designs and implements AI and machine learning capabilities for cyber and intelligence workflows. The role translates mission-user processes into LLM-powered, agentic, or hybrid solutions; integrates AI with APIs, enterprise data, and mission platforms; and ensures reliable, secure production deployment in classified environments. Collaboration with analysts, operators, developers, and leadership is central. Required qualifications include Python, TS/SCI with polygraph, and relevant AI/ML or software engineering experience.

Lead end-to-end machine learning architecture, production systems, forecasting, probabilistic modeling, experimentation, and agentic AI development. Build models for acquisition cost, lifetime value, ROAS, demand, and market outcomes; establish reproducible data and model pipelines; create evaluation harnesses for LLM agents; and own calibration, feedback loops, measurement, and cost controls. Mentor cross-disciplinary engineers and influence technical strategy across teams.

Develop and deploy production-grade AI/ML solutions on Microsoft Azure. Build secure data pipelines, prepare and validate datasets, support NLP and machine learning workloads, and implement MLOps practices including CI/CD, monitoring, troubleshooting, and model lifecycle management. Collaborate with engineers, analysts, and stakeholders in Agile teams while producing architecture, testing, security, and operational documentation for regulated government environments.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Develops personalized advertising recommendation algorithms using machine learning and deep learning. Responsibilities include identifying modeling opportunities, defining research roadmaps, developing and validating new models with large-scale user data and compute, analyzing metrics, and partnering with engineering and product teams to productionize research and deliver measurable business results.

Lead the development of ads recommendation and personalization models using machine learning, deep learning, user behavior data, and large-scale computing. Responsibilities include defining modeling opportunities and roadmaps, conducting foundational research, validating new models, analyzing metrics and data, and partnering with engineering and product teams to productionize research and deliver measurable business results.

Build and calibrate an agent evaluation platform for multi-step LLM agent trajectories. Design rubrics, deterministic validators, LLM judges, confidence scoring, and human calibration workflows. Fine-tune judge models, analyze simulation-versus-production divergence, and develop process reward models for agent optimization. Establish versioned scenarios, simulated environments, and reliable measurement standards while maintaining production-grade Python code.

Owner of product features for Card Intelligence ML initiatives; collaborate with data scientists, engineers, and stakeholders to define roadmaps, break down epics into MVPs, prioritize features, and accelerate model-to-market delivery for fraud prevention.

Build, deploy, and operate production AI applications and scalable machine learning solutions. Develop automated training and deployment workflows, support production AI infrastructure, and collaborate with software, data science, and platform engineering teams. The role involves Python development, ML frameworks, containerization, Kubernetes, Linux, and Git, with preferred experience in LLMs, RAG pipelines, model serving, vector databases, frontend development, and ML orchestration.

Designs and builds high-performance data infrastructure for machine learning model training and feature serving. Responsibilities include developing distributed processing architectures, optimizing training data pipelines, resolving performance bottlenecks, collaborating with ML research teams, and establishing tooling and best practices for ML data infrastructure. The role requires experience with fault-tolerant distributed systems and frameworks such as Apache Spark or Flink.

Optimize large-scale machine learning training systems across CPUs, GPUs, and accelerators. Responsibilities include benchmarking, distributed-training optimization, GPU kernel and framework development, end-to-end pipeline efficiency, numerical optimization, and training infrastructure. The role partners with ML researchers, quantitative researchers, HPC engineers, systems engineers, and hardware specialists to improve performance, scalability, utilization, reproducibility, and cost across on-premises and cloud environments.

Develop and maintain Retrieval-Augmented Generation pipelines, LLM integrations, REST APIs, and self-hosted LLM deployments. Conduct LLM alignment red teaming, independently research emerging AI/ML methods, and develop advanced AI applications. The role requires strong Python and AI/ML framework expertise, with preferred experience in model evaluation, fine-tuning, inference optimization, vector databases, AWS, Docker, and scalable data architectures.

Develops and evaluates artificial intelligence and machine learning algorithms for U.S. Government mission systems. Responsibilities include selecting and analyzing data, training and improving models, conducting testing and validation, overseeing system integration, researching AI/ML methods, converting prototypes into scalable solutions, and verifying data and model quality. The role requires collaboration, independent work, and support for critical software and systems under a government contract.

Develop machine learning and generative AI methods to optimize chip layout for performance, power, and area. Automate physical design, verification, and exploratory PDK generation; build benchmarking, modeling, and QA systems; and evaluate advanced device architectures and process technologies for sub-2nm nodes. Collaborate with global R&D organizations and research consortia, transfer technology to Samsung’s Korea-based development teams, and guide cross-functional technical initiatives.

Own the technical vision and system architecture for Digit’s safety-critical perception systems in dynamic environments. Lead architectural tradeoffs across sensing, ML models, redundancy, latency, compute, and robustness. Define verification and validation standards, support safety cases and hazard analysis, evaluate emerging technologies, and collaborate with navigation, controls, hardware, and safety teams. Mentor senior engineers and represent perception architecture to executives, customers, regulators, and partners.

Design, train, and deploy machine learning and perception models for edge-intelligent maritime systems. Optimize inference pipelines for embedded hardware, develop datasets and labeling strategies, support computer vision, signal processing, and sensor-fusion experiments, and build real-time on-device and cloud data pipelines. Create benchmarking and debugging tools, evaluate emerging ML research, and collaborate across hardware, software, and product teams to deliver robust production AI solutions.

Supports R&D teams developing computer vision and machine learning algorithms for UAV-mounted cameras and robotic platforms. Responsibilities include data preparation, model development, testing, visualization, performance evaluation, error analysis, and deployment strategy research. The intern will write and test software, conduct experiments, analyze datasets, and collaborate on focused design teams in an onsite aerospace and defense environment.

Develop AI/ML solutions for semiconductor design and verification workflows. Build scalable pipelines that extract and analyze EDA logs, reports, run artifacts, and design metadata. Create models, heuristics, and analytics for STA, DRC/LVS, ECO optimization, debug, and regression acceleration. Integrate solutions into CAD infrastructure, automation systems, schedulers, and design databases while collaborating with EDA vendors.

Develop and productionize AI/ML solutions for healthcare, focusing on multimodal LLMs, conversational AI, document understanding, search, and Q&A. Responsibilities include evaluating and fine-tuning models, building training data pipelines, managing the full ML lifecycle, deploying scalable solutions, troubleshooting integrations, documenting architecture, and ensuring HIPAA compliance. The role requires strong Python and PyTorch skills, experience with LLM training platforms and cloud ML services, and close collaboration in a fast-paced startup environment.