Top Entry Level Machine Learning Jobs

Develop search relevance algorithms for an enterprise question-answering platform. Create and optimize ranking features, build frameworks for diversified ranking, design tooling for parallel ranking experiments, and develop metrics to evaluate search quality. Analyze query logs to identify improvements and collaborate with product, UX, customer success, and engineering teams to deliver accurate answers across heterogeneous knowledge sources.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, quality engineers, and product owners to design, implement, test, and deliver scalable software. Apply high-performance algorithms, core AI/ML techniques, object-oriented programming, unit testing, profiling, and code tuning to improve user experiences and workflow efficiency.

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 audio and multimodal machine learning models on embedded and edge devices. Build real-time inference pipelines, convert models to efficient C/C++ implementations, and optimize latency, memory, SRAM, and power across MCUs, DSPs, NPUs, and accelerators. Collaborate with researchers, DSP experts, firmware engineers, and hardware teams to deliver production-ready embedded AI systems using model development, quantization, profiling, and on-device runtimes.

Architect, deploy, and maintain production-grade AI and machine learning systems across GenAI and classical ML projects. The role owns technical architecture, monitoring, alerting, model lifecycle management, cloud deployment, MLOps practices, and continuous improvement. Responsibilities also include guiding project teams, making strategic technology decisions, translating AI concepts into business value, evaluating practical applications of research, and communicating technical recommendations to engineering and business stakeholders.

As an Applied Scientist, you'll develop and deploy ML models, enhance pricing systems, and work with structured and unstructured data to improve decision-making.

The Applied Scientist will tackle various machine learning problems, enhance valuation systems, and deploy deep learning models while collaborating with cross-functional teams.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Lead Aaru’s Simulation Engineering team and own the quality, scalability, reproducibility, observability, and operational health of production AI simulations. Set architecture and productionization standards, build evaluation and regression systems, improve large-scale simulation performance, manage incidents and releases, collaborate across research and engineering, and recruit and develop high-performing engineers. The role remains hands-on, involving technical design, coding, debugging, and difficult tradeoffs among fidelity, calibration, latency, cost, and reliability.

Develop and deploy LLM workflows and production machine learning models for nuclear-system design and analysis. Build surrogate models for real-time digital twins, manage data pipelines, maintain rigorous information controls, and lead AI initiatives from inception through delivery. The role also involves growing a software engineering team, presenting roadmaps and demos to leadership, gathering user feedback, and collaborating with nuclear engineers in a fast-paced startup environment.

Quantitative Researchers will build models and strategies for pricing and trading financial instruments, collaborating closely with teams and using various techniques from statistical methods to deep learning.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

Join Optiver as a Graduate Quantitative Researcher to analyze large-scale market and order-flow data, develop predictive and stochastic models, apply machine learning and deep learning techniques, and translate research into live trading solutions.

Conduct applied quantitative research on market and order-flow data: develop and validate predictive, statistical, and stochastic models using machine learning and deep learning, test hypotheses, and deploy ideas into live trading environments to identify trading opportunities and manage risk.

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.

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

Develops, trains, tests, deploys, and maintains machine learning models for mission-critical intelligence community space projects. Applies ML algorithms and cloud technologies, collaborates with data scientists, physicists, and systems engineers, and contributes to agentic AI and ML strategy. Requires cloud, Kubernetes, programming, machine learning, bachelor’s degree, and TS/SCI clearance experience.

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.

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.

Develop and deploy ML and HPC workflows, algorithms, and software for DOE scientific applications. Collaborate with domain scientists, scale ML on heterogeneous architectures, ensure correctness and reproducibility, profile and optimize performance, apply software-engineering best practices, and publish and present research. Two-year postdoctoral appointment with possible extension.

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.

Design, develop, and deploy data-driven solutions across product lifecycle. Partner with design, manufacturing, test, and quality teams. Mine and analyze company data, build custom models and algorithms, monitor model performance, and propose continuous improvement strategies. Integrate with vendor software and support product modifications and maintenance.