Top Machine Learning Jobs

Architect and build terabyte-scale robotics data pipelines from fleet sensor streams through training ingestion. Curate, preprocess, annotate, version, and validate datasets; analyze data coverage, drift, redundancy, and quality; mine scenarios using embeddings and models; and develop samplers, benchmarks, and evaluation datasets. Partner with ML researchers to diagnose model failures and create targeted data mixtures and processing workflows that improve robot-learning performance.

Design, implement, debug, and refine advanced neuro-symbolic AI and machine learning systems. Collaborate with researchers, engineers, and subject-matter experts to develop deployable solutions for national security and commercial applications. Responsibilities include leading R&D programs, managing technical collaborations, writing reports and papers, engaging customers, and advancing deep learning, probabilistic reasoning, symbolic knowledge, optimization, and related technologies from research through deployment.

Leads Aptive’s AI strategy and roadmap while personally delivering production machine-learning systems for forecasting, route optimization, retention, pricing, lead qualification, and customer and technician support. Builds and manages the ML and data science team, partners with data engineering on infrastructure, establishes model deployment and monitoring practices, and oversees responsible AI governance. The role requires measurable business impact, executive-to-engineering communication, and hands-on model and pipeline review.

Design, train, deploy, and optimize computer vision models for automated vehicle damage detection. Build high-throughput containerized microservices using REST/gRPC, maintain Kafka data pipelines, and own the full machine learning lifecycle from experimentation through production optimization. Collaborate with stakeholders to develop scalable, reliable inspection solutions and lead technical design efforts.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.

Develop and productionize ML models across the full stack, optimizing training and inference for GPUs, NPUs, and AI silicon. Build inference runtimes, MLOps pipelines, embedded system integrations, verification harnesses, and security-focused ML capabilities. Collaborate with hardware, RTL, firmware, and QA teams to deploy AI features under real-world latency, memory, power, and reliability constraints.

Develop and ship computer vision and machine learning features for an AI-enabled CAD system. Responsibilities include data and labeling strategy, model selection and fine-tuning, evaluation pipeline development, 3D perception using LiDAR and imagery, production model adaptation, and customer-driven iteration. The role requires collaborating with product, engineering, customer-facing teams, and surveyors to deliver extraction workflows end to end.

Builds and deploys AI-enabled applications, machine learning models, data pipelines, APIs, and decision-support tools for aggregate and asphalt operations. The role optimizes plant automation, production efficiency, uptime, quality, pricing, sales processes, and costs using operational, equipment, quality, GIS, and sales data. It integrates AI with plant systems and legacy software, collaborates across operations and IT, and ensures responsible AI, cybersecurity compliance, documentation, and user adoption.

Own the machine-learning signals and decisions governing web crawling and indexing at massive scale. Develop classifiers, rankers, and calibrated models to assess page quality, credibility, freshness, parsing success, duplicate content, and indexing value. Define ground truth for ambiguous problems, validate measurable search improvements, and collaborate across crawling, indexing, and retrieval teams to improve the quality and coverage of search results.

Develop large-scale machine learning systems for web content understanding and search quality. Responsibilities include parsing web pages, classifying content, extracting metadata, assessing quality and credibility, detecting misinformation and AI-generated content, deduplicating documents, designing supervision for unlabeled problems, and building efficient models that operate across the web in every language and format.

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 and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Design datasets, evaluation methods, scoring rubrics, and reward signals for machine learning model training. Build data pipelines for natural and synthetic training material, develop quantitative methods to assess dataset quality and model impact, study annotation behavior, run controlled experiments, and collaborate with research partners to define training and evaluation specifications.

Design, train, deploy, and operate machine learning systems for real-time suspicious-activity detection. Own model development, data pipelines, serving, infrastructure, scalability, and production measurement. Collaborate with technical leadership and customers to identify threats and improve detection signals. Establish engineering standards, ensure correctness, and deliver reliable systems in a high-autonomy, fast-moving team.

Instructor-led AI and machine learning training covering theory, data preprocessing, algorithms, model building, analytics, real-time projects, case studies, and practical business and IT applications. Participants use Python, TensorFlow, and Scikit-learn and receive certification upon successful completion.

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.

Lead technical direction for machine learning engineering and data systems. Build pipelines that transform unstructured laboratory data into structured datasets, develop and deploy models, validate measurable performance improvements, and run discovery workflows for commercially relevant findings. Initially hands-on, the role expands to hiring and leading an engineering team while developing backend infrastructure that supports scientific and application teams.

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.

Leads applied AI/ML science and engineering initiatives, developing machine learning models for healthcare outcomes using structured and unstructured data. Establishes reusable AI/ML platforms, production pipelines, and engineering standards; mentors scientists and engineers; collaborates with business and responsible AI teams; and promotes enterprise adoption. The role also involves technical documentation, patents, publications, model governance, and explaining complex solutions to technical and non-technical audiences.

Lead technical development of onboard motion-planning systems for low-speed autonomous vehicle maneuvering. Improve evaluation capabilities, investigate driving issues through log analysis, develop foundational frameworks, solve challenging long-tail driving scenarios, and mentor junior engineers. The role also includes managing a team of at least five reports and collaborating across engineering teams.

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.

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 integrate Atlas humanoid robot agent systems across harness design, context engineering, tool use, memory, evaluation, observability, dashboards, and diagnostics. Develop reliable planning and reasoning capabilities using VLMs and agentic systems, benchmark performance and safety, collaborate with robotics and product teams, and ship full-stack applications for real-world use cases.