Top Machine Learning Jobs

Build and productionize machine learning systems that convert complex documents into executable, verifiable logic. Responsibilities include developing parsing and transformation pipelines, experimenting with language models, designing datasets and evaluation metrics, creating adversarial tests, analyzing model failures, and implementing rule-based checks using logical reasoning and program analysis. The role requires close collaboration with founders and subject-matter experts to validate system outputs.

Design, train, evaluate, and deploy machine learning models that assess complex technical work across images, geometry, and interaction logs. Translate expert judgment into training signals, labels, datasets, and evaluation criteria. Manage the full learning loop, analyze model failures, collaborate with domain specialists, and deliver production models for live decision-making using Python and PyTorch.

Build production recommendation, scoring, optimization, and reinforcement-learning systems that adjust advertising spend, budgets, and campaign status. Design feedback loops and multi-agent reasoning systems using live results and shared context. Own decision quality and financial outcomes while applying statistics, A/B testing, and advertising optimization concepts such as pacing, budget allocation, ROAS, and CPA targets.

Build backend services, data pipelines, retrieval systems, and machine learning inference pipelines for large-scale video, audio, and text data. Integrate language and vision-language models into production, deploy secure distributed systems, and own projects from architecture through release. The role also requires clear technical communication, strong Python skills, backend scaling expertise, and comfort in a fast-paced startup environment.

Develop production machine learning and software capabilities, including bespoke models, ranking and scoring systems, measurement frameworks, data pipelines, and resilient AI workflows. The role requires independent product judgment, customer collaboration, broad full-stack development, and ownership of core engineering foundations as the company’s first engineering hire.

Lead product strategy and execution for signals exploitation and tracking capabilities. Define roadmaps, shape backlogs with customers and product owners, support architecture and design reviews, and drive development and demonstration of advanced algorithms and software for DoD and Intelligence Community missions. Balance reuse, integrated system offerings, and component supply-chain opportunities while leading engineering teams through the Agile lifecycle and supporting technical marketing and business development.

Designs, develops, deploys, and operationalizes machine learning solutions using Python, PySpark, Azure technologies, and large-scale telemetry datasets. Builds scalable data pipelines, trains and evaluates models, applies MLOps practices, and communicates insights through dashboards and presentations. Collaborates with data engineers, data scientists, architects, product owners, and business stakeholders to deliver enterprise machine learning solutions.

Lead hands-on development and deployment of large multimodal machine learning models for physical hardware. Responsibilities include data preparation, neural network training, evaluation, real-world testing, hyperparameter experimentation, GPU infrastructure design, and improving model reliability, latency, and experimentation throughput across multiple workstreams.

Build and deploy production multimodal machine learning systems for images and video, including model orchestration, evaluation, fine-tuning, edge inference, runtime optimization, and interactive audio-visual interfaces. The role involves deploying open-weight models on constrained or on-premise hardware, creating agent workflows and feedback loops, and moving prototypes into dependable field use for hardware-based products.

Build and operate production machine learning systems and MLOps infrastructure for financial services. Responsibilities include developing feature and training pipelines, model-serving systems, CI/CD, automated retraining, monitoring, alerting, observability, and progressive model rollouts in GCP and Kubernetes. The role partners with data scientists and business teams to improve reliability, performance, scalability, cost efficiency, and deployment velocity while applying AI coding agents to infrastructure and ML workflows.

Design, train, and deploy machine learning models and perception software for autonomous aircraft. Develop real-time AI algorithms using deep learning, 3D computer vision, and sensor fusion for navigation and detect-and-avoid systems. Build model training, evaluation, data management, simulation, and analysis tooling while collaborating with software, controls, sensing, and flight engineering teams. Optimize production autonomy systems and mentor engineers through strong software practices.

Set technical vision for Pinterest’s Product ML Infrastructure, leading distributed model training, fine-tuning, evaluation, and high-scale CPU/GPU inference. Improve GPU efficiency, data loading, execution, kernels, memory, compilation, quantization, scheduling, and capacity. Build reliable, observable training and serving platforms, drive architecture decisions and migrations, partner with AI/ML teams, mentor senior engineers, and establish operational standards across the organization.

Develop software and applied RF machine learning algorithms for wireless systems. Responsibilities include SDR prototyping, signal processing, software integration, database interfaces, data workflows, testing, documentation, technical presentations, and research-to-prototype delivery. The role collaborates with researchers and engineers in a customer-facing, classified environment and may lead research activities.

Build and deploy production-grade AI systems for regulated institutions. Own ambiguous ML problems end-to-end, including data definition, model training, evaluation, deployment, and monitoring. Work with LLMs, vision models, reinforcement learning, agentic systems, and rule engines to process complex documents and support institutional decisions. Collaborate directly with customers, engineer for accuracy, latency, cost, and reliability, and develop rigorous evaluations and failure-mode taxonomies.

Own the end-to-end machine learning lifecycle, including data preparation, dbt pipelines, feature engineering, model development, deployment, monitoring, and evaluation. Apply ML to risk assessment, cost prediction, image analysis, and business operations. Contribute to architecture and strategy while partnering with Engineering, Product, Operations, and Business teams. Build scalable solutions using Python, AWS, Kafka, Django or FastAPI, PostgreSQL, and GitHub Actions.

Develops machine learning software algorithms, supports data pipeline design, integrates model pipelines, monitors data quality, and engages with stakeholders for analytic solutions.

Develops AI/ML algorithms and ETL infrastructure supporting LLM, NLP, predictive analytics, and other customer solutions. Builds and maintains data pipelines, curates evaluation data, improves scalability and automation, and collaborates with researchers and developers using Agile methods. The role requires Python-based data engineering, machine learning experience, and an active TS/SCI clearance, with preferred experience in cloud, containers, distributed computing, reinforcement learning, GenAI operations, and GPU programming.

Design, develop, and deploy production-grade AI/ML solutions for national security missions. Build NLP, LLM, computer vision, deep learning, and generative AI models; scalable data pipelines; APIs; microservices; and cloud or edge-deployed services. Optimize, version, evaluate, and monitor models while integrating them into secure operational environments. Collaborate with engineers, mission experts, and product teams to define requirements, document designs, and deliver reliable AI capabilities.

Designs, develops, deploys, and integrates production-grade AI/ML systems for national security missions. Responsibilities include building NLP, LLM, computer vision, and deep learning models; developing scalable data pipelines, APIs, microservices, and cloud or edge services; optimizing and evaluating models; and applying MLOps, security, reliability, and software engineering practices. The role collaborates with mission experts and engineering teams and requires an active TS/SCI clearance.

Build and deploy production machine learning systems across computer vision, multimodal modeling, structured data, and agentic AI. Own projects from problem definition through monitoring, develop data and evaluation pipelines, establish feedback loops, and translate experiments into practical technical recommendations. Collaborate with product, engineering, operations, annotation teams, and domain experts while delivering maintainable code, documentation, and scalable ML solutions.

Develop and deploy production-grade AI and machine learning capabilities for national security and OPIR missions. Responsibilities include signal-processing automation, data processing, model development, evaluation, MLOps workflows, GPU optimization, streaming-data integration, and scalable deployment in operational environments. The role involves collaboration with engineers, mission subject-matter experts, and product teams, with emphasis on AI agents, NLP, LLMs, computer vision, microservices, and classified cloud environments.

Design, develop, and deploy production-grade AI/ML solutions for national security missions, including NLP, LLMs, generative AI, deep learning, and computer vision. Build scalable data pipelines, APIs, microservices, MLOps workflows, and cloud or edge-deployed systems. Optimize and evaluate models, integrate them into secure operational environments, and collaborate with engineering and mission teams. Document technical designs and support accreditation processes in classified settings.

Own and advance a production physician-organization matching engine from rules and weights to semantic matching, embeddings, and vector search. Build rigorous precision/recall evaluation, entity resolution across 8M+ NPI records, and a reliable lightweight data platform using healthcare data. Independently own ingestion, enrichment, modeling, serving, and architecture decisions while improving recommendation quality and supporting customer-facing reporting.

Serve as a technical partner to sales and customer success teams, advising high-value customers on cloud infrastructure and AI/ML solutions. Responsibilities include architecture reviews, proofs of concept, demos, onboarding, technical workshops, infrastructure optimization, LLM deployment and fine-tuning, GPU workload optimization, AI application design, data pipeline and database integration, escalations, documentation, and development of tools for premium customers. Collaborate globally with Engineering, Product, Support, and account teams.

Build, train, deploy, and maintain large-scale machine learning models and AI agents for marketing and customer experiences. Develop conversational AI, generative AI, recommendation, retrieval, ranking, and transformer-based systems. Own ML product lifecycles, including success metrics, A/B testing, production operations, and iterative improvement. Collaborate with Engineering, Product, Design, customers, and stakeholders while leading technical projects and mentoring teammates.