Top Senior Machine Learning Jobs

Research and develop ML and statistical algorithms for networking, edge computing, cybersecurity, and autonomy. Build and evaluate simulation-driven solutions, lead subsystem software development, create automated tests, and contribute to proposals and technical plans while presenting results to customers and senior staff.

Morgan Stanley seeks a Machine Learning Researcher to solve critical problems with innovative research. Responsibilities include applying machine learning techniques and strong communication skills within a collaborative team. Ideal candidates have 4+ years of research experience and proficiency in statistical learning methods.

Lead technical strategy and build production-scale ads ranking and bidding models using large-scale ML and agentic development loops. Design training, evaluation, and feedback pipelines that learn from online behavior and experimentation, ensure reliability, fairness, and compliance, and partner with product, science, and infra teams to drive metrics and mentor engineering talent.

The Senior Python Engineer designs, develops, and deploys machine learning solutions, ensuring scalability and integration into production systems, while collaborating with engineering and data teams.

Lead architecture and delivery of end-to-end AI/ML solutions for a MediaOS platform: personalization, recommendation, targeting, forecasting, scalable ML pipelines, cloud-native distributed systems, MLOps, secure AI, and mentorship of engineers and data scientists.

Lead end-to-end ML initiatives from prototyping to large-scale production, build and deploy LLM-powered and multimodal content intelligence systems, design evaluation frameworks and experiments, partner with cross-functional teams to set technical strategy, and mentor engineers while adopting AI-assisted development tooling.

Develop and prototype learning-based models using data-centric techniques; design and maintain multi-modal data and feature extraction pipelines; build auto-labeling systems; perform ETL and optimize SQL for analysis/ingestion; collaborate with data infrastructure and ML engineers to integrate workflows; write maintainable code and participate in code reviews to support scalable ML production.

Lead technical direction for autonomous mineral refining by designing, validating, and deploying RL-based control models. Own autonomy roadmap, set simulator and modeling standards, solve sim-to-real and non-stationarity problems, partner with domain experts and leadership on techno-economic decisions, and mentor engineers while delivering production ML that runs on live plant equipment.

The Senior Machine Learning Engineer will enhance HP's retrieval systems and recommendation algorithms, focusing on AI-driven user interactions and system performance improvements.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Senior Machine Learning Engineer at FINNY, you will design, train, and improve ML models for various applications, and work closely with other teams to optimize performance and ensure effective deployment.

The role involves developing advanced machine learning algorithms, solving complex optimization problems using reinforcement learning techniques, and leading technical projects while optimizing solutions for operational efficiency.

As a Senior Machine Learning Scientist, you will develop multimodal foundation models using large-scale single-cell datasets to advance drug discovery, applying deep learning methods and collaborating with biologists and engineers.

The role involves developing ML-based planning algorithms for autonomous trucks, ensuring safety checks, real-time operation, and keeping up with research. Requires strong communication and collaboration.

Design, integrate, and optimize AI/ML components for scalable, production systems. Improve latency, throughput, reliability, and resource efficiency. Ensure operational readiness, security, monitoring, deployment, and troubleshooting while collaborating with cross-functional partners.

Lead development and production deployment of ML products: perform exploratory analysis, build and evaluate models, design experiments and metrics, leverage generative AI and graph-based methods, collaborate with product and engineering, influence strategy, and maintain robust CI/CD and platform practices.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Lead post-training for LLMs (CPT, SFT, RL) focusing on RL methods; design and curate training and reward data; run and debug distributed training on mid-to-large GPU clusters; build evaluation and verifier pipelines; partner with product and business teams to deliver targeted model improvements quickly.

Lead and grow a team of ML research scientists to design, implement, and deploy deep learning and LLM-based recommender and real-time bidding systems. Provide strategic direction, mentor researchers, contribute hands-on to coding and experiments, collaborate with engineering to enable scalable deployments, and manage project timelines to deliver high-impact research into production.

As a Senior ML Engineer for Payments, you will develop AI solutions for payment experiences, optimize transaction processing, and ensure system reliability while mentoring teammates and collaborating with cross-functional teams.

Lead architecture and productionization of scalable ML/LLM systems for legal products. Build LLM applications (RAG, agents), hybrid search, APIs, streaming/data pipelines, containerized deployments, and observability. Partner with data scientists to turn models into reliable, low-latency customer-facing services and establish reusable frameworks, best practices, and technical leadership across teams.

The role involves developing machine learning algorithms to enhance customer experience. Responsibilities include end-to-end ML lifecycle management, validating models, and collaborating with teams to address complex problems.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

Design, prototype, and operationalize ML models and data pipelines for temporal, geospatial, and track-based mission data. Build data processing, feature engineering, containerized cloud deployments (AWS/Docker/Kubernetes), APIs, and scalable evaluation/monitoring workflows while following Agile and software engineering best practices.

As a Senior Machine Learning Research Engineer, you will research and implement advanced audio ML models, develop inference algorithms, and collaborate with the Operations team to enhance model quality.