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.

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

Develop and scale machine learning systems to enable AI coaching, collaborate across teams for member insights, and contribute to architectural decisions.

The Senior Machine Learning Engineer designs and develops ML systems, collaborates with teams to meet analytical needs, and optimizes large-scale ML solutions while setting monitoring and best practices.

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.

Develop, prototype, test, and maintain mathematical algorithms and software; analyze and interpret data using mathematical and statistical techniques; evaluate computational resources and promote appropriate mathematical tools across teams.

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

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.

Improve enterprise search QA by designing and optimizing ranking features and algorithms, building experiment tooling and platforms, evaluating ranking metrics, and partnering with product, UX, and customer teams to boost answer quality across heterogeneous knowledge sources.

Design, deploy, and lead scalable AI/ML solutions and data engineering efforts for life sciences clients. Mentor teams, ensure architecture and coding best practices, support business development, and collaborate with clients on implementation and 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.

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.

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 validate quantitative models and automated trading algorithms for securities trading. Conduct research on trading cost, liquidity, risk models, portfolio construction, and signal generation. Collaborate with stakeholders, present model results, and implement process improvements while adhering to regulatory and risk frameworks.

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.

Lead program management for Core Algorithm Development, coordinating algorithm, data science, firmware, software, product, and validation teams to plan, validate, integrate, and release physiological algorithms while improving processes, mitigating risks, and reporting progress to executives.

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.

Lead AML model development, optimization, validation, and governance for transaction and customer screening across banking, brokerage, and lending. Build ML/statistical models, maintain vendor solutions, ensure regulatory compliance, document methodologies, produce AML dashboards, and support audits, risk assessments, and stakeholder communications.

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.

Design, develop, and deliver technical solutions and AI-based models to scale business workflows. Collaborate with stakeholders to capture requirements, test and validate models, drive Aladdin Compliance and Accounting platform enhancements, engage internal technology partners, and implement automations (including RPA) and data visualizations to improve process efficiency.

Lead modelling and analysis roadmap for FX products: forecast and monitor FX volatility, optimise trade execution with statistical/ML models, run customer and profitability analyses, and partner with product, trading, risk, and engineering to translate insights into trading and product decisions.

Develop and deploy machine learning systems for Mars missions, enhance atmospheric modeling, and integrate multi-modal data for autonomous science.

The role involves developing machine learning systems for Mars atmospheric modeling and multi-modal data fusion, contributing to autonomous science in space missions.

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