Top Machine Learning Jobs

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Develop, validate, and deploy credit underwriting machine learning models. Lead proofs-of-concept for alternative data, collaborate cross-functionally on model deployment, ensure regulatory and fair-lending compliance, and present model insights to credit and business leaders.

Lead product vision and execution for a portfolio of ML-powered personalization and insights products. Translate research and analytics into roadmaps, run experiments, partner with data science and engineering, ensure privacy/fairness, enable self-serve platforms, and measure outcomes across marketing, servicing, assistants, and financial management.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Validate and challenge ML/NLP/Generative and Agentic AI models across the model lifecycle: review data and sampling, replicate models, develop tests and monitoring, build reusable validation code, write analytical reports, coach junior staff, and communicate with stakeholders, regulators, and auditors.

Lead Gusto's AI/ML strategy, managing teams in Machine Learning Engineering, AI, and Risk Data Science. Ensure systems are reliable and impactful while fostering a culture of technical excellence.

Lead end-to-end machine learning projects to improve customer engagement and retention. Build, test, and deploy models for user forecasting, personalization, and Casino engagement. Partner cross-functionally to scale solutions, mentor junior data scientists, and communicate model impact to influence product and marketing strategy.

Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

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.

Lead and direct multidisciplinary teams to research, develop, test, integrate, and demonstrate advanced signal processing, optimization, and machine learning algorithms for radar, electronic warfare, and sensor systems. Oversee modeling/simulation, algorithm T&E, customer engagement, and technical briefings for defense and intelligence applications.

The role involves developing LLM applications for automated silicon design, optimizing models, curating datasets, and collaborating with other experts to integrate LLMs into EDA workflows.

Develop, implement, calibrate, and validate statistical, machine learning, and AI models for risk management. Use Python/R/SAS/C++/SQL and big-data tools, perform model validation, document and present results, recommend process improvements, and work with regulators and auditors. Hybrid role based in Charlotte, NC.

Perform data cleaning, exploratory analysis, and build/productionize ML and statistical models (including NLP) to generate business value for service and fluids-analysis domains. Collaborate with cross-functional teams, present findings to stakeholders, and work independently on analytics projects within an Agile environment.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Lead development and deployment of optimization and machine learning models to improve retail banking decisions. Translate business problems into mathematical formulations, build production-grade optimization and simulation models, collaborate with Product and Engineering to integrate solutions, and run end-to-end model development, evaluation, validation, and implementation.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

Develop and deploy econometric and machine learning models for credit risk, loss forecasting, CECL/allowance, stress testing, and capital allocation. Partner with business lines to build cloud-based analytical solutions, automate processes, communicate results to stakeholders, and own the full model lifecycle from data exploration through deployment and monitoring.

Develop, validate, and maintain RMBS prepayment and credit/default models; perform large-scale data processing and ML analysis; build analytics platforms and monitoring tools; support valuation, hedging, risk assessment, model review, and client/internal training.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Lead the design, development, and deployment of advanced machine learning solutions at Optum Tech, mentoring junior engineers and collaborating across teams.

Design and maintain data pipelines and BI infrastructure, ensure data quality, optimize SQL and transformations, build and validate predictive and ML models, support deployment and BI integration, and collaborate with stakeholders to deliver scalable analytics solutions.

Lead product strategy and execution for signals exploitation and tracking capabilities. Define roadmaps, shape backlogs with customers and product owners, support architecture and design reviews, and drive development and demonstration of advanced algorithms and software for DoD and Intelligence Community missions. Balance reuse, integrated system offerings, and component supply-chain opportunities while leading engineering teams through the Agile lifecycle and supporting technical marketing and business development.

This internship focuses on researching low-latency ML inference and hardware acceleration, involving project ownership, collaboration with engineers, and presenting findings.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.