Top Entry Level Machine Learning Jobs

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 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 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.

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.

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 role involves developing synthesis-aware virtual screening workflows for small-molecule drug discovery, integrating various modeling and design strategies.

Apply machine learning and AI models to real-world problems by designing, building, and deploying software systems. Implement end-to-end model workflows, ingest and transform large datasets, research and test advanced topics, verify software meets requirements, follow QA processes, document findings, and support engineering teams.

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.

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.

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

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

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.

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.

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

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.

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.

Contribute to building, training, evaluating, deploying, and monitoring ML and LLM systems across production platforms. Support ML pipelines, work with large-scale datasets, assist in LLM, retrieval, ranking, recommendation, and agentic workflows, and collaborate with cross-functional teams to integrate AI capabilities.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Design and evaluate next-generation recommendation and ranking models using LLMs, RLHF, and preference learning. Build user understanding systems for conversion and value estimation at scale. Apply RL and optimization to bidding and real-time delivery. Run causal inference, A/B tests, and offline evaluations. Collaborate with engineering to productionize research and communicate results to technical and non-technical stakeholders.

Design, build, and evaluate next-generation ranking and recommendation models using LLMs, RLHF, and preference learning. Develop user understanding systems for conversion and value estimation at massive scale. Apply reinforcement learning and optimization to bidding and auction dynamics. Design and run causal inference and A/B experiments. Partner with engineering to productionize models and communicate results to technical and non-technical stakeholders.

Design and deploy next-generation recommendation and ranking models using LLMs, RLHF, and preference learning. Build user-understanding systems, apply RL and optimization to bidding and ad delivery, run causal A/B experiments, and collaborate with engineering to productionize research across the ML pipeline.

Develop and productionize consumer-facing AI features using LLMs. Collaborate across the AI stack, run A/B tests and evaluations, improve model reliability and guardrails, and optimize latency, cost, and scalability of production AI systems.

Work alongside full-time ML researchers on end-to-end projects applying state-of-the-art models to large, nonstationary market datasets. Tasks include data exploration, model development, hyperparameter tuning, debugging training, and analyzing model predictions using large-scale compute and GPU resources. Internship-focused, with heavy emphasis on practical ML implementation and iteration.