Top Entry Level Machine Learning Jobs

Conduct research and develop machine learning models for finance, focusing on deep learning and large language models, optimizing them for performance, and collaborating with teams to integrate ML solutions.

Build reliable machine learning and data systems that transform assay videos, scientific data, and experimental outcomes into reproducible models, evaluated predictions, and experiment recommendations. Responsibilities include data and feature pipelines, model training and evaluation infrastructure, batch and online inference systems, monitoring, data quality, versioning, observability, and interfaces across computer vision, molecular modeling, active learning, and laboratory workflows.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Develops deep learning and machine learning models for computer vision, synthetic aperture radar, and geospatial exploitation applications. Responsibilities include designing neural network architectures, training models for object detection and classification, preprocessing SAR data, creating physics simulations, addressing simulated-to-real-world bias, evaluating model performance, presenting findings, and delivering production-quality Python code. The role supports mission-critical defense and national security customers.

Research Engineer responsible for developing and productionizing post-training methods for diffusion and flow models, including reward modeling, preference optimization, supervised fine-tuning, distillation, and reinforcement learning. The role owns research planning, evaluation standards, training pipelines, and the feedback loop between model behavior and product data. Models will be deployed to professional designers, requiring rigorous engineering, reproducible evaluations, and close collaboration with product and founders.

Develop and maintain production-scale AI/ML systems and infrastructure supporting Navy and DoD cyber defense, situational awareness, mission assurance, and command-and-control capabilities. Responsibilities include software design, coding, testing, debugging, deployment, ML model monitoring, data engineering, application lifecycle support, security architecture reviews, documentation, and agile team participation.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

Design, develop, deploy, and iterate production machine learning and generative AI solutions. Responsibilities include model development, NLP, prediction, anomaly detection, LLM and RAG integration, data pipelines, feature engineering, evaluation, monitoring, API integration, and architecture decisions. The role collaborates cross-functionally to balance accuracy, latency, cost, scalability, and maintainability.

Role involves assisting with machine learning model improvement through tasks like data labeling, content evaluation, and participation in testing activities.

Open general application for Rapsodo, a sports analytics company using computer vision and machine learning. Seeking driven team players passionate about sports and technology to contribute to state-of-the-art analytics solutions and growth across product teams. Applicants encouraged to apply to join a fast-paced, innovative company partnered with major sports organizations.

Build and deploy computer vision and machine learning solutions from research prototypes into scalable production services. Develop MLOps pipelines for data preparation, training, validation, versioning, deployment, and monitoring. Train and optimize deep learning models, integrate them into secure cloud systems, and support national security missions. Collaborate with mission partners, engineers, and domain experts while applying containerization, DevSecOps, model governance, and inference optimization practices.

Design, develop, deploy, and monitor end-to-end AI and machine learning solutions for clients. Build scalable cloud architectures, optimize RAG pipelines, create retrieval evaluation frameworks, design knowledge graphs, develop data ingestion pipelines, deploy secure self-hosted models, and protect PHI/PII. Advise cross-functional teams, mentor colleagues, and contribute to technical thought leadership.

Build and operationalize LLM applications for financial services. Responsibilities include developing production AI features, Python libraries, data pipelines, prompts, tool-calling workflows, retrieval systems, evaluation suites, monitoring, and responsible-AI safeguards. Diagnose hallucination, latency, and cost issues; define metrics; analyze failures; collaborate with product, engineering, and business teams; and document designs and findings.

Design, develop, evaluate, and refine machine learning models using Python and leading ML frameworks. Analyze datasets with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and provide data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize models in cloud or enterprise environments.

Develop, optimize, package, validate, and deploy AI/ML applications in secure, mission-critical environments. Collaborate with analysts and platform, infrastructure, and security teams; support MLOps, CI/CD, distributed systems, troubleshooting, performance tuning, accreditation, compliance, audits, and technical documentation. The role uses machine learning frameworks including TensorFlow, PyTorch, Scikit-learn, Keras, computer vision, and GANs.

Develop and optimize machine learning and statistical models to predict liquid asset movements. Parse and analyze large datasets, implement academic research for alpha generation, and collaborate with Data, Alpha, and Strategy teams to design market-neutral trading strategies and run simulations to validate hypotheses.

Develop, test, and apply quantum computing workflows or quantum-inspired approaches to analyze sensing and energy-industry datasets. Work may include signal processing and machine learning. Produce technical documentation, presentations, and publishable research (journal or conference paper).

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Build and deploy low-latency machine learning models and inference infrastructure for Netflix’s advertising platform. Develop identity resolution, behavioral and contextual targeting, lookalike expansion, user signals, and audience features at massive scale. Collaborate with science, product, engineering, operations, design, and research teams to productionize models while supporting privacy, governance, security, and advertiser brand safety.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

Research and develop multimodal perception and authentication systems using visual, audio, and sensor data. Build specialized and multimodal models, design training and evaluation methods, analyze robustness and failure modes, and integrate capabilities into real-time, resource-constrained systems. Collaborate with hardware, firmware, software, and product teams to transition research into practical products.

Design and architect recommendation algorithms, build data pipelines and continuously learning training jobs, improve ranking systems through real-time experimentation and user feedback, and ensure machine learning systems are scalable and efficient. The role requires industrial-scale recommender systems or deep learning experience and proficiency with data infrastructure and deep learning frameworks.

Entry-level ML engineer to assist in developing, training, deploying, and monitoring machine learning models. Responsibilities include data cleaning, building pipelines, supporting production deployments, troubleshooting model performance, collaborating with team members, and documenting code and processes.