Top Entry Level Machine Learning Jobs

Develop and optimize CUDA kernels and machine learning software for training and inference on advanced GPUs. Profile workloads, improve GPU utilization, build benchmarking and automation tools, troubleshoot system bottlenecks, support new model architectures, validate numerical and gradient correctness, review code, and contribute to technical documentation and industry events.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Develop, optimize, and deploy production machine learning models for autonomous vehicle perception, prediction, planning, and scene understanding. Own the full ML lifecycle, build scalable training and data pipelines, optimize neural networks with quantization and TensorRT, and integrate models into C++ vehicle systems. Analyze simulation and real-world driving data, improve reliability and latency, and collaborate with autonomy, hardware, systems, and infrastructure teams.

Build machine learning infrastructure spanning large-scale data generation, multi-GPU LLM training, training and inference optimization, and production deployment and monitoring. The role also contributes to AI detection research, publications, and technical innovation. Engineers will develop scalable data pipelines, manage distributed systems, deploy efficient LLM inference services, and collaborate across research and engineering teams in an in-person Brooklyn office.

Designs, develops, tests, deploys, and improves AI/ML models supporting business and national security mission requirements. Responsibilities include selecting datasets, performing statistical analysis, training and retraining models, integrating data pipelines, monitoring performance, applying testing protocols, and overseeing system integration. The role requires knowledge of model architecture, data engineering, infrastructure, governance, and ML libraries, with potential leadership of distributed teams and customer-facing AI initiatives.

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 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 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.

Develop and productionize machine learning models for industrial process optimization using spectral and multimodal sensor data. Responsibilities include designing experiments, building preprocessing and feature extraction pipelines, monitoring model and sensor drift, detecting anomalies, developing ML infrastructure, and applying chemometrics, hybrid modeling, self-supervised learning, probabilistic reasoning, and time-series techniques.

Develop advanced personalization and recommendation systems for digital grocery shopping. Build, deploy, monitor, and maintain machine learning pipelines and microservice APIs supporting production inference. Apply natural language processing, generative AI, large language models, agentic AI, recommendation models, deep learning, transformers, and classical machine learning techniques to real-world business problems. Evaluate and tune models, communicate technical results, and contribute to scalable AI solutions.

Design, develop, test, evaluate, deploy, and maintain AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Process structured and unstructured data, build ETL pipelines, conduct experiments, measure performance, deploy models in cloud environments, document findings, brief stakeholders, educate staff, and provide AI/ML subject matter expertise.

Develop detection, classification, image analysis, and AI/ML algorithms for security screening systems. Build data analysis, testing, validation, and model improvement tools; research emerging machine learning and deep learning methods; and collaborate with engineering teams to transition concepts from research into production. The role also requires technical documentation, stakeholder communication, and contributions to aviation and homeland security technologies.

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.

Build next-generation voice AI systems for consumer and business applications. Develop persistent memory, complex reasoning, speech pipelines, and production-grade agentic workflows. As an early technical contributor, the ML Engineer will help shape core architecture, take ambiguous concepts through production, and influence user interactions with advanced language models in a fast-paced pre-seed startup.

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

Lead end-to-end machine learning architecture, production systems, forecasting, probabilistic modeling, experimentation, and agentic AI development. Build models for acquisition cost, lifetime value, ROAS, demand, and market outcomes; establish reproducible data and model pipelines; create evaluation harnesses for LLM agents; and own calibration, feedback loops, measurement, and cost controls. Mentor cross-disciplinary engineers and influence technical strategy across teams.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.

Develops personalized advertising recommendation algorithms using machine learning and deep learning. Responsibilities include identifying modeling opportunities, defining research roadmaps, developing and validating new models with large-scale user data and compute, analyzing metrics, and partnering with engineering and product teams to productionize research and deliver measurable business results.

Build, fine-tune, deploy, and optimize production machine learning systems. Design scalable training, inference, evaluation, monitoring, and data pipelines; integrate models with enterprise systems and APIs; improve performance, reliability, latency, throughput, and cost efficiency; and collaborate with research, product, and engineering teams to deliver end-to-end AI features.

Conduct applied AI and machine learning research for aerial, maritime, and underwater autonomous systems. Develop computer vision, multimodal perception, sensor fusion, reinforcement learning, and uncertainty-aware models; integrate them with physical platforms and autonomy stacks; run experiments; and build evaluation pipelines and operator-facing tools. The role also involves technical reporting, proposal development, stakeholder briefings, student mentoring, and collaboration with faculty, defense partners, and Navy program stakeholders.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Designs, develops, tests, deploys, trains, monitors, and improves machine learning models for business and national security mission requirements. Responsibilities include selecting datasets, statistical analysis, algorithm execution, model evaluation, data pipeline integration, system testing, and scalable solution development. The role requires strong understanding of AI/ML architectures, application development, infrastructure, data engineering, and governance, with potential leadership of distributed teams.

Design, deploy, optimize, and maintain advanced machine learning models for monetization applications. Translate AI research into scalable production systems, build data pipelines, improve model performance, collaborate with research, engineering, product, and design teams, and contribute to model monitoring, code reviews, and end-to-end problem ownership.