Top Machine Learning Engineer Jobs

The Machine Learning Engineer will develop and improve ML models for product surfaces, build data processing pipelines, and leverage machine learning technologies in advertisements.

Develop data-driven solutions using Python and R; perform statistical analysis, data wrangling, and machine learning model development with TensorFlow and Scikit-Learn. Collaborate with cross-functional teams to deliver actionable insights, predictive analytics, and automation. The role also requires software engineering experience, familiarity with agentic AI frameworks or coding agents, and an active TS/SCI clearance with polygraph.

Develop data-driven solutions using Python and R; conduct statistical analyses; design and train machine learning models with TensorFlow and Scikit-Learn; wrangle and prepare raw data; and collaborate with cross-functional teams to deliver actionable insights. The role requires extensive software engineering experience, agentic AI or AI coding-agent expertise, active TS/SCI clearance with polygraph, and a relevant bachelor's degree or equivalent experience.

Lead design and operation of a centralized AI and data platform for autonomy: define compute strategy, build distributed training and simulation systems, manage multi-modal datasets and MLOps, enable model evaluation, optimization, and deployment to constrained edge and customer environments, and drive platform standardization and broad adoption across programs.

Lead productionization and optimization of ML models for a global, serverless inference platform. Build benchmarking/evaluation tooling, improve inference performance (quantization, compilation, batching), integrate models with heterogeneous GPUs and accelerators, enhance deployment workflows and observability, and mentor engineers while driving ML production best practices.

Operationalize and scale machine learning, generative AI, and agentic solutions for biologics discovery. Build production pipelines, deployment and serving capabilities, lifecycle management, monitoring, observability, versioning, governance, CI/CD, and scalable compute workflows. Partner with data scientists, AI/ML practitioners, technology teams, and scientific domain experts to transition models from experimentation to reliable production use.

Designs, builds, and deploys Python-based NLP and machine learning workflows, knowledge graphs, analytical tools, APIs, and collaborative TypeScript applications. Integrates data feeds, supports secure end-to-end pipelines, prototypes solutions in ambiguous environments, and partners with Joint Staff customers in classified SIPRNet and JWICS environments. The role requires adapting technologies to mission-specific analytical and operational needs while providing documentation and technical support.

Develop scalable ML infrastructure for autonomous vehicle use cases within Uber AV Labs. Responsibilities include designing multi-region, multi-cloud platforms; customizing Uber’s ML platform; aligning infrastructure and AV teams; executing technical projects from conception through production; and optimizing GPU efficiency. The role requires expertise in CUDA, TensorRT, Kubernetes, Flyte or Airflow, and Ray, along with at least six years of experience and a computer science degree.

Build production-grade AI and NLP systems that verify generated claims against scientific, clinical, regulatory, and reference materials. Responsibilities include hybrid evidence retrieval, claim decomposition, entailment and contradiction detection, confidence scoring, abstention logic, human review workflows, evaluation dataset creation, error analysis, and traceable citation-based decisions. Collaborate with Medical, Legal, Regulatory, and scientific stakeholders to deliver high-stakes, reproducible AI solutions.

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Own evaluation systems and model-quality improvements for a generative content platform producing AI-generated ecommerce content. Build datasets, rubrics, automated judges, quality gates, and labeled training data from brand feedback. Quantify qualitative improvements, establish approval thresholds, and drive fine-tuning or retrieval experiments into production. The role requires strong software and system design skills, production service ownership, formal machine learning or statistics training, and experience measuring correctness in non-deterministic systems.

Leads the design, development, integration, verification, and flight-test delivery of machine learning and autonomy capabilities for uncrewed aircraft. Responsibilities include perception, tracking, sensor fusion, planning, mission execution, model deployment, real-time C++ and Python software, simulation, testing, troubleshooting, and cross-functional system integration. Provides technical leadership and mentorship without direct personnel management. Requires on-site aircraft integration and field-test support in Bingen, Washington.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core, including vector and matrix engines. Own functional features, performance, power, area, timing, and physical design considerations. Develop specifications, optimize RTL, support verification, and collaborate on CPU modeling, design verification, and SoC integration. The role requires deep expertise in high-performance CPU architecture, cache and memory subsystems, SystemVerilog or related HDL technologies, and advanced processor design.

Build scalable full-stack software for AV data introspection, visualization, search, and analysis. Partner with Product, Design, and Engineering stakeholders to define and deliver a roadmap, address customer workflow gaps, lead technical discussions, and promote engineering solutions. The role requires developing reliable applications for diverse data types, working with SQL and NoSQL architectures, and integrating AI techniques into front-end experiences.

Develop and deploy perception, generative AI, and online mapping algorithms for L4 autonomous trucks. Responsibilities include building robust real-time software for safety-critical systems, integrating maps and vehicle sensors, improving mapping performance, leading features from concept through delivery, and coordinating stakeholders. The role requires deep learning model development and deployment, applied research, machine learning algorithm design, Python, and C++, with sensor fusion or online mapping experience preferred.

Lead technical discovery, demonstrations, proof-of-concepts, and post-sales engagements for Lightning AI’s machine learning platform. Translate customer requirements into scalable AI solutions, support enterprise evaluations, partner with sales and cross-functional teams, influence product direction, and drive customer adoption. The role also involves coaching technical field leaders, supporting forecasting and deal execution, and contributing to solutions engineering growth in a fast-moving startup.

Design and build scalable ML infrastructure and backend systems for high-impact marketplace applications. Lead architecture, solve low-latency and high-correctness problems, develop and adopt engineering frameworks, and create production-ready technical roadmaps. Partner with Product, Science, and Operations while mentoring engineers and improving engineering standards. The role requires experience with ML models, foundational models or LLMs, MLOps, Kubernetes, data pipelines, distributed workflows, and large-scale scheduling.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.

Build and operate production small language models for real-time AI governance. Responsibilities include model training, fine-tuning, distillation, high-performance inference, synthetic-data generation, evaluation, telemetry, adversarial testing, failure diagnosis, and closed-loop model improvement. The role also provides technical leadership and partners with product, security, research, and customer-facing teams to deploy reliable, low-latency models at enterprise scale.

Build and operate production ML infrastructure across cloud, on-premises, GPU, edge, and disconnected environments. Own model training and serving, CI/CD, reproducibility, evaluation, observability, data pipelines, retrieval systems, and secure IL5/IL6 deployments. Support accreditation and classified environments while improving reliability, quality, and performance of LLM and ML systems.

Develops and sustains cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, installation, enhancement, maintenance, and issue resolution. The role works with distributed NoSQL systems, big-data technologies, cloud scalability, machine learning, large language models, RAG, agentic workflows, microservices, Kubernetes, observability, and infrastructure-as-code tools.

Design, train, evaluate, deploy, and own production machine learning models for transaction categorization, risk scoring, and financial data enrichment. Build scalable ML systems and automated data pipelines across large, diverse datasets. Partner with product, data science, and engineering teams, improve model reliability and accuracy, explore emerging AI techniques, and mentor engineers.

Develop, evaluate, and deploy reinforcement learning methods for autonomous vehicle planning. Design real-time model architectures, build offline data-driven ML infrastructure, analyze fleet and field-testing data, optimize and deploy planning models, and collaborate with perception, software, and AI teams to deliver production autonomous driving systems.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.