Top Entry Level Machine Learning Jobs

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.

Own machine learning modeling for a software platform, adapting large language models, building training data pipelines, and developing evaluation methods. Run experiments, refine models, and collaborate with researchers and systems engineers to deploy dependable models in production. The role focuses on electronic design automation and hardware design data, including netlists, RTL, surrogate models, and graph neural networks. It also involves post-training LLMs, distributed training or inference, and model optimization.

Develops search relevance solutions for an enterprise question-answering platform. Responsibilities include creating and optimizing ranking features, building frameworks for diversified ranking, enabling parallel ranking experiments, and developing metrics to evaluate search performance. The role applies information retrieval, machine learning, text processing, and natural language understanding to address lexical gaps, semantic understanding, and data sparsity while collaborating with product, UX, customer success, and engineering teams.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

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.

Design, develop, test, and deploy AI/ML services and agentic workflows for rugged tactical networking and computing systems. Translate business needs into technical requirements, collaborate with technical owners, and build scalable applications using Python, Linux, cloud-native infrastructure, AI frameworks, databases, observability tools, and CI/CD pipelines. The role supports mission-critical government, defense, and intelligence applications.

Early-career engineer responsible for characterizing AI/ML training and inference workloads, analyzing compute and memory bottlenecks, and building performance models for hardware and software systems. The role partners with architecture, compiler, runtime, and product teams on optimization decisions, benchmarking, and reproducible technical analyses. Candidates apply mathematical reasoning, computer architecture knowledge, Python-based data analysis, and machine learning frameworks. Travel is required up to 10%.

Develop, train, evaluate, debug, and improve machine learning models for real-world applications. Own the ML lifecycle from dataset construction and experimentation through deployment, while applying deep learning, reinforcement learning, and LLM techniques. Collaborate with engineering and product teams, contribute to scalable ML infrastructure and tooling, define evaluation metrics, analyze errors, and translate research concepts into practical solutions.

Lead the technical direction for ML-powered experimentation and predictive insights. Build production systems spanning modeling, LLMs, retrieval, simulation, statistical methods, data pipelines, backend services, and product workflows. Develop traceable, calibrated, privacy-aware predictions; establish rigorous evaluation, backtesting, monitoring, and abstention practices; and translate insights into approval-gated experimentation workflows. Partner with engineering, product, data science, and research teams while providing technical leadership for high-quality production ML.

Build, deploy, and maintain production machine learning models and end-to-end ML pipelines. Develop MLOps frameworks with model versioning, CI/CD, registries, feature stores, monitoring, and automated retraining. Deploy solutions using cloud and containerized technologies, troubleshoot performance issues, address model drift, and collaborate with engineering and data teams on scalable mission-critical applications.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Develop physics-informed machine learning models for electronic systems. Build automated electrical, thermal, and electromagnetic simulation workflows, generate datasets, create surrogate models, and validate predictions against solvers and experiments. Develop active learning and optimization workflows while owning reproducible pipelines for data generation, training, evaluation, and deployment. Collaborate with hardware and machine learning teams to accelerate electronics development.

Develop and implement frontier-model post-training methods, including reinforcement learning, verifiable rewards, evaluation, and data-centric optimization. Design rigorous experiments across datasets, rewards, environments, and training strategies; investigate model behavior and failure modes; build scalable data-generation and evaluation pipelines; and create benchmarks, rubrics, and scoring systems. Collaborate with researchers, engineers, AI teams, customers, and domain experts while contributing to open-source tools, technical reports, blog posts, and research papers.

Develop and implement statistical and machine learning trading strategies and quantitative models. Design optimization algorithms for portfolio construction and explore new research opportunities within derivatives and market behavior.

Develop and deploy computer vision modules and machine learning models within AI pipelines. Integrate models with data and software engineering teams, support prototypes, automate data pipelines, build internal tooling, and monitor production performance. The role also involves optimization, infrastructure and security support, staying current with computer vision advancements, and collaborating cross-functionally.

Run and evaluate machine learning training experiments for autonomous robots, curate and clean real-world fleet data, improve data-labeling structures, track model performance metrics, and collaborate with Teleoperations to improve data collection. The role focuses on production ML for perception, decision-making, and control, including imitation learning, reinforcement learning, and deployment evaluation on live robotic systems.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Develop machine learning and personalization systems for real-time advertising at planetary scale. Conduct architectural research on attention-based and post-attention models, evaluate hypotheses offline and online, improve recommender and targeting systems, and transition successful research into production. Build model implementations and data pipelines, guide infrastructure investments, collaborate with engineering and product teams, and contribute to the research community through publications, talks, and conferences.

Train and fine-tune diffusion models, adapters, and pipelines for character-based sticker generation. Build provenance-aware training data pipelines and evaluation systems measuring character fidelity and output quality. Partner with brand teams to secure character approvals, ensure reliable outputs from spoken requests, and collaborate with serving and trust-and-safety engineers to launch safe products for children.

Build and deploy production machine learning systems, primarily recommendation and personalization models. Own projects across problem definition, data pipelines, model training, deployment, monitoring, and optimization. Develop scalable low-latency inference infrastructure, run experiments and A/B tests, and apply ML to fraud detection, abuse prevention, and content moderation. Collaborate with engineering, product, and data science teams in a fast-paced, in-person startup environment.

Build and improve AI recruiting agents using modern LLMs, Python backend services, APIs, prompts, RAG, workflows, and agent orchestration. Evaluate model performance, experiment with AI technologies, collaborate with product and engineering teams, and deploy scalable production systems. The role requires Python, LLM and AI API experience, familiarity with agent frameworks and FastAPI, and cloud platform knowledge. Production AI product experience, model evaluation, tool calling, prompt engineering, and open-source contributions are preferred.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

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.