Top Machine Learning Engineer Jobs

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.

Build data preprocessing and generation pipelines, run model training and fine-tuning workflows, design benchmarking and evaluation suites, and operationalize research models into reproducible production workflows. The role collaborates with researchers, communicates metrics and results, and owns measurable machine learning outcomes from data preparation through delivery.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

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 end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Develop mathematical models for music preferences, similarity, engagement, personalization, and discovery. Build and deploy scalable real-time recommendation and ranking systems, translate product objectives into metrics, run large-scale experiments and causal analyses, and collaborate with product and research leaders on personalization strategy.

Develop and scale machine learning and optimization systems for Uber's rider pricing, discounts, promotions, and personalized messaging. Build real-time and offline models and infrastructure, apply deep learning and generative AI, optimize Spark-based data workflows, and collaborate with product and science teams to deliver measurable revenue and growth outcomes.

Lead machine learning and optimization initiatives for Uber’s rider pricing systems. Develop, improve, evaluate, and deploy scalable pricing and promotion-targeting algorithms. Own technical problems end to end, shape product and technical roadmaps, collaborate across ML, serving, and product teams, and mentor engineers. Apply deep learning, generative AI, causal modeling, representation learning, personalization, and reinforcement learning to improve pricing, affordability, ridership, and revenue.

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.

Design and deploy ML and NLP models (including LLM evaluation) to predict borrower risk and property valuations, advise business teams, mentor junior analysts, and foster data-driven experimentation.

Develop and improve reinforcement learning infrastructure for Waymo’s autonomous driving machine learning systems. The intern will support LPM model training, profile and improve training efficiency, integrate new reinforcement learning technologies, and collaborate on large-scale ML infrastructure. Candidates should be pursuing a master’s or PhD in a relevant technical field and have knowledge of machine learning, deep learning, reinforcement learning, modern ML frameworks, and Python software development.

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.

Own and improve production machine learning models through retraining, failure analysis, data curation, experimentation, and releases. Develop scalable labeling workflows, including active learning and LLM-assisted review; expand models to audio, video, and tabular data; apply rules-based methods, classical ML, fine-tuning, and LLMs to entity recognition and resolution; and build evaluation benchmarks and experiment tooling.

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.

Develop and deploy machine learning capabilities for radar systems, including labeling automation, active learning, classification, tracking, data augmentation, and self-supervised models. Build training and evaluation pipelines, assess robustness across rare targets and field conditions, analyze failures, and integrate models into production radar products. Optimize accuracy, latency, computation, and reliability for embedded, resource-constrained systems.

Develop deep learning models using imitation and reinforcement learning to generate and evaluate autonomous vehicle trajectories. Build production machine learning pipelines, metrics, and error-analysis tools, leveraging large-scale infrastructure. Collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving behavior in complex urban environments.

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.

Build shared machine learning capabilities for AI-native platform products, including document extraction, foundation models, model routing, evaluation, agentic systems, and continuous improvement. Translate urgent product-team needs into reusable platform abstractions, own capabilities through production, optimize accuracy, latency, cost, and reliability, and develop feedback and retraining loops that improve deployed systems across regulated institutional workflows.

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

Develop AI/ML algorithms and ETL infrastructure for LLM, NLP, and time-series solutions. Build scalable data pipelines, curate evaluation datasets, implement machine learning models, optimize data delivery, automate processes, and develop software using Agile methodologies. The role collaborates with researchers and developers and may involve cloud services, distributed computing, containerization, GenAI operations, reinforcement learning, and GPU-based algorithm implementation.

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.