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Top Machine Learning Jobs in Pittsburgh, PA
As a Staff Machine Learning Engineer at Handshake, you will lead a small team to enhance AI products and infrastructure, focusing on Generative AI and Recommendations. You will develop machine learning systems, design partnerships across teams, and drive technical direction while ensuring high-quality execution and user-centered solutions.
The Sales Development Representative will identify and solicit new customer accounts, generating leads for the Regional Sales Team. Responsibilities include managing the initial sales process for EHR solutions, conducting cold calls, achieving sales targets, and maintaining relationships with customers.
The Senior AI/ML Engineer will design and implement machine learning algorithms, maintain ML system pipelines, and integrate AI components into various applications. Collaborating with cross-functional teams, the engineer will also manage version control, implement CI/CD pipelines, and design RESTful APIs, all while working in fast-paced, regulated environments.
As a ML Solutions Engineer, you will act as a trusted advisor to customers, engaging them with product education and managing escalations while influencing product roadmaps. Your role involves gathering goals and KPIs with pre-sales engineering, demonstrating AI product value, and developing relationships with technical stakeholders.
The Principal Engineer, AI/ML will lead competencies in machine vision, NLP, and generative AI. Responsibilities include designing and fine-tuning machine learning models, deploying and monitoring models on the cloud, and ensuring scalable and robust systems and APIs.
As a Technical Program Manager at Coinbase, you will lead machine learning focused programs, ensuring strategic alignment and roadmap delivery. You'll work with cross-functional teams, manage documentation and communication, monitor program status, and address issues proactively. Additionally, you will need to communicate effectively with both technical and non-technical stakeholders.
This role involves leading the development of AI/ML technologies and platforms, designing architecture, implementing infrastructure, collaborating with various teams, managing project roadmaps, and driving the technical vision and strategy of AI/ML within the organization. Additionally, it encompasses mentoring engineering teams and ensuring continuous improvement in quality, security, and performance metrics.
phData is looking for Sr Machine Learning Engineers to design and create environments for data scientists, work within customer systems, define deployment approaches, reveal the value of data, partner with data scientists, and create operational testing strategies. Apply for future opportunities at phData even if there's no current role available.
The Product Manager will expand Zingtree's AI-based product portfolio, driving new product development and enhancements. Responsibilities include collaborating with engineering, understanding customer needs, crafting business plans, and staying updated on AI technology and market trends.
As a Machine Learning Engineer for the Advertiser Budget Optimization team at Reddit, you will build and optimize advertising budget systems using advanced techniques, collaborate on technical designs, and analyze experiments to enhance monetization efficiency. Your role involves working with real-time auction data and engaging with various teams for system improvements.
As a Senior Manager in AI/ML Engineering, you will lead teams of machine learning engineers and data scientists to deliver scalable AI and machine learning solutions. You will advocate for engineering best practices, coach engineers, and drive innovative solutions to enhance Nike's supply chain with advanced analytics.
The ML Engineer - Inference at Symbl.ai will develop and optimize deep learning models for real-time communication applications, focusing on the Nebula language model. Responsibilities include designing algorithms, integrating models into production, optimizing for various environments, and evaluating model performance, all while collaborating with cross-functional teams.
The Principal Machine Learning Engineer will lead the development of AI/ML solutions for Twilio's communication platform, focusing on NLP and real-time decision-making. Responsibilities include architecting scalable ML systems, collaborating on strategic initiatives, and ensuring compliance with data security and ethical AI practices.
As a Product Manager at ABBYY, you will drive product strategy and execution, analyze market performance, define product roadmaps, and collaborate with marketing and sales teams to deliver high-quality product releases.
The Solutions Architect - AI/ML at Snowflake will leverage expertise in data science and cloud architecture to enhance customer engagement and implement solutions for AI/ML workloads. Responsibilities include advising on best practices, building ML pipelines, and collaborating with various teams to optimize Snowflake's capabilities for clients.
As a Machine Learning Engineer specializing in natural language processing at Entefy, you will use deep learning and various methodologies to develop innovative algorithms that enhance communication technologies. Your responsibilities will include working on confidential high-impact projects and applying your knowledge in machine learning theory and text analytics.
Join Hims & Hers Analytics and Data Science team to engage in data-driven decision making, improve user experience, and contribute to product development. Collaborate with business leaders, marketers, and product teams to drive analytical insights, and leverage machine learning for innovative solutions across the organization.
As a Lead AI ML Engineer, you will design, develop, and deploy machine learning models and data pipelines, drive impactful business decisions, and manage the data infrastructure in collaboration with data engineers and scientists while ensuring high-quality outputs.
As a Tech Lead Manager for Securities Engineering, you will oversee a technical team to develop and manage high-performance trading systems. Your role involves mentoring engineers, developing roadmaps, and ensuring system reliability while collaborating with research and operations teams.
As a Staff Machine Learning Data Scientist, you will lead the development of machine learning tools for patient data, integrating insights from diverse stakeholders, and conducting complex analyses in cloud environments. You'll collaborate with experts to provide innovative solutions and generate novel clinical insights from extensive patient data.
Lead the design and development of robust data architectures that support scalable, secure, and efficient data pipelines. Drive innovation, mentor team members, ensure data quality and resilience, and collaborate with cross-functional teams to deliver impactful data solutions.
As an ML Platform Engineer at Stitch Fix, you'll build and maintain infrastructure for machine learning, develop APIs and platforms, and support data scientists. You'll collaborate with teams to improve systems and foster technical collaboration, enhancing the velocity of innovation within the organization.
As a Staff Machine Learning Engineer at Lily AI, you will design scalable machine learning systems, optimize deep learning models, and implement best practices in ML Ops. You'll collaborate with various teams to define the ML roadmap, improve system performance, and develop tools to enhance team productivity. The role emphasizes automating workflows and transitioning prototypes to production-ready systems.
The Senior Software Engineer will collaborate with experts to build scalable machine learning solutions for autonomous systems, focusing on data management and workflow orchestration. Responsibilities include designing and implementing cloud services and optimizing training pipelines.
As a Senior Machine Learning Engineer, you will develop, train, document, deploy, and manage production machine learning models. You will collaborate with internal and external customers to build scalable models and support other teams in the Foundation while ensuring the effectiveness of machine learning solutions.
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