Top Machine Learning Jobs

Develop and implement AI/ML algorithms across computer vision, NLP, time series, reinforcement learning, and large language models. Analyze multimodal datasets, apply research findings, document systems, support AWS-based training and deployment pipelines, and mentor team members. The role requires substantial AI/ML development experience, advanced Python and machine learning library proficiency, deep learning expertise, and an active TS/SCI clearance.

Develop and implement AI/ML algorithms for defense and commercial applications, analyzing image, text, and graph datasets to build actionable models. Use cloud services to train and deploy models, apply statistical and machine learning techniques, review research, and create technical documentation. The role involves collaboration with software developers and researchers and requires an active TS/SCI clearance with CI Poly.

Design, deploy, and scale production AI/ML solutions for healthcare, including predictive models, generative AI, statistical analysis, microservices, APIs, and MLOps pipelines. Analyze complex clinical datasets, implement model monitoring and governance, evaluate emerging AI technologies, and collaborate with engineering, data, product, and clinical teams to improve care delivery and operational efficiency.

Build and operate production machine learning systems enabling autonomous medical coding at scale. Responsibilities include data discovery, feature engineering, model training and evaluation, deployment, monitoring, MLOps pipelines, LLM integration, observability, governance, fairness assessment, and A/B testing. The role also involves designing highly available low-latency architectures, defining technical roadmaps, and collaborating with product, design, data, and platform teams.

Lead the technical strategy, architecture, modernization, and company-wide adoption of Pinterest’s AI/ML evaluation platform. Design reliable evaluation systems for GenAI and machine learning workloads using automated metrics, human feedback, expert judgment, safety assessments, and business outcomes. Drive cross-team workstreams, resolve technical tradeoffs, establish evaluation standards, and improve the quality, reliability, and trustworthiness of AI-powered products.

Leads Pinterest’s Ads Quality machine-learning organization, setting technical strategy across engagement, conversion, ranking, ROAS, retrieval, foundation models, and experimentation. Manages engineering managers and senior technical leaders while improving model quality, production reliability, latency, privacy, cost efficiency, and engineering velocity. Partners across product, data science, infrastructure, and advertising organizations to connect modeling investments with user value, advertiser performance, revenue, and marketplace health.

Design and implement scalable offline and online machine learning infrastructure for search and recommendation. Own data logging, processing, quality, model training, batch and real-time pipelines, and online inference services handling billions of records and petabytes of data. Partner with data scientists, analysts, product managers, and engineering teams while researching technical solutions and mentoring engineers.

Sets technical vision for Pinterest’s ML and media infrastructure, leading distributed training, fine-tuning, evaluation, inference, GPU optimization, content ingestion, and signal-processing architectures. Partners with AI/ML, Ads, Content, Shopping, and GenAI teams to productionize reliable, scalable platforms, drive architecture decisions and migrations, improve operational standards, and mentor senior engineers.

Own the architecture, deployment, performance, and reliability of Stickerbox’s production machine learning pipeline. Optimize latency, cost, batching, caching, GPU utilization, model serving, and inference across image generation, speech recognition, and character models. Lead deployment strategies, capacity planning, observability, safety enforcement, automated quality checks, migrations, release engineering, and on-call operations for a global consumer hardware launch.

Leads the Machine Learning Engineering team supporting League of Legends. Owns ML engineering vision, strategy, MLOps, model lifecycle management, production reliability, experimentation, and measurable player impact. Manages engineers, budgets, recruiting, staffing, mentoring, performance, and organizational planning while partnering with product and technical leaders to deliver scalable ML-powered experiences and tools.

Lead architecture and development of advanced perception systems for Level 4 autonomous vehicles. Build multimodal foundation models, synthetic data and sensor simulation capabilities, knowledge-distillation pipelines, and efficient real-time onboard models. Deploy solutions to autonomous vehicle fleets while mentoring engineers and providing technical leadership. The role focuses on object detection, segmentation, tracking, classification, sensor fusion, closed-loop evaluation, and robust generalization across vehicle generations.

Develops and leads production machine learning and image processing solutions for neurovascular OCT and angiographic medical imaging. Responsibilities span algorithm research, prototyping, software architecture, implementation, testing, validation, deployment, and maintenance within regulated medical device systems. The role provides technical mentorship, contributes to ML frameworks and infrastructure, collaborates cross-functionally, performs code reviews, and supports quality, documentation, and compliance initiatives.

Owns the end-to-end improvement of machine learning models after initial training, including compute setup, dataset preparation, simulated environment design, and automated self-improving workflows. The role combines applied AI research with software engineering, requiring expertise in reinforcement learning, evaluations, interpretability, PyTorch, ML pipeline debugging, and curated dataset production.

Develop and deploy machine-learning and autonomous-driving algorithms for parking assistance and low-speed autonomy. Responsibilities include perception, localization, sensor fusion, planning, state estimation, threat assessment, deep-learning model development, scalable training pipelines, data-centric optimization, and real-world validation. The role collaborates across software, systems, validation, and vehicle integration teams while supporting safety-critical production deployments through a rigorous V-cycle process.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Lead the design, optimization, and production deployment of high-performance LLM inference systems using vLLM, CUDA, Triton, C++, and Python. Improve GPU utilization, latency, throughput, scalability, and cost through kernel optimization, quantization, batching, memory management, distributed inference, and profiling. Contribute to upstream open-source projects, establish engineering standards, review performance-critical code, and guide research-driven serving improvements.

Design, develop, test, debug, and maintain scalable software applications and prototypes using Java and Python. Work in a Linux full-stack environment, integrate customer-tailored large language models, design accessible interfaces, connect external requirements tools, troubleshoot performance issues, conduct code reviews, support planning, and mentor junior engineers. The role requires extensive software engineering experience, a bachelor’s degree or equivalent experience, DoD8570 IAT2 compliance, and TS/SCI clearance with polygraph.

Design and build a production-grade AI memory and retrieval engine for personalized, privacy-preserving LLM experiences. Responsibilities include developing multi-tier storage and hybrid retrieval pipelines, memory extraction and classification, adaptive updates, evaluation frameworks, and reliability safeguards. The role researches advanced memory architectures, supports agentic workflows and tool integrations, collaborates with product management, and owns production operations. Strong software engineering, Python, NLP/ML, vector database, LLM orchestration, privacy, and RAG expertise are required.

Design and build production-grade AI memory systems, including retrieval pipelines, storage tiers, privacy filtering, memory extraction, adaptive updates, hybrid search, and evaluation frameworks. Ship and maintain LLM-powered features, diagnose RAG and prompt failures, implement agentic workflows and tool integrations, and ensure reliable degradation and fallback strategies. Collaborate with product management to define feasible memory-system capabilities while owning production reliability.

Designs, builds, deploys, and operates mission-grade AI/ML capabilities, including scalable data pipelines, model training, REST inference services, cloud-native MLOps, and production monitoring. The role manages AWS workloads, containerized deployments, infrastructure as code, DevSecOps, data governance, encryption, compliance evidence, and end-to-end model lifecycles. It applies supervised, unsupervised, reinforcement learning, NLP, computer vision, and generative AI to mission use cases in accredited cloud environments.

Design, develop, deploy, and optimize scalable machine learning models and workloads on cloud platforms. Build ML scoring processes, APIs, microservices, automated workflows, monitoring, and alerting. Support ML go-to-market initiatives and collaborate with Product and Engineering teams on compliant architecture and deployment. The role requires Databricks, cloud ML, end-to-end ML systems, data pipelines, FastAPI, data warehouses, Docker, Kubernetes, and strong software engineering practices.

Design, train, evaluate, and deploy production machine learning, deep learning, and reinforcement learning systems for semiconductor manufacturing. Build RL environments, reward functions, policies, agents, simulation workflows, and scalable MLOps pipelines. Research generative AI, LLMs, RAG, and multi-agent systems while partnering with software engineers, data scientists, cloud architects, and semiconductor experts to integrate intelligent capabilities into production solutions.

Build and scale pipelines that transform conversation data into structured customer memory. Develop LLM extraction and retrieval systems using embeddings, vector search, and ranking; create evaluation benchmarks and regression gates; diagnose production issues through logs, metrics, and traces; and partner with Product and ML teams to improve customer-focused AI systems.

Leads the design, development, deployment, and operation of production ML, LLM, and generative AI systems. Sets technical direction, establishes engineering and MLOps standards, mentors engineers, evaluates and fine-tunes generative models for NLP, and implements monitoring for reliability. Partners with technical and business stakeholders to prioritize use cases and communicate AI capabilities. Develops scalable pipelines, microservices, and model lifecycle governance while applying current AI research and external APIs.

Build and optimize scalable pre-training pipelines for foundation models. Implement distributed training across GPUs, TPUs, and high-performance clusters; translate research experiments into production workflows; develop monitoring and fault-tolerance systems; and benchmark and tune performance across hardware and software stacks.