Collaborate with data scientists and engineers to build scalable ML pipelines, troubleshoot infrastructure issues from Linux to Kubernetes, and optimize model performance.
Drive high engineering standards, design on-premises MLOps solutions, and maintain tools for deployment and monitoring.
Refine CI/CD workflows, incorporate ML model training and evaluation into testing, and ensure seamless handover between research and production.
Learneo is a platform of builder-driven businesses, including Course Hero, CliffsNotes, LitCharts, Quillbot, Symbolab, and Scribbr, focused on supercharging productivity and learning. The company supports high-growth businesses with centralized corporate operations and has a virtual-first culture with employees across multiple countries.
Develop AI-powered applications and workflows from idea to production, balancing quality, latency, reliability, and cost.
Integrate Large Language Models, APIs, RAG approaches, and automation into existing products and processes, learning from real user signals.
Build robust ML pipelines, collaborate with data scientists and engineers, and shape the technical direction for AI solutions.
Breuninger is a fashion and lifestyle company operating 13 department stores and online shops across multiple European countries. With 6,500 employees, the company fosters a friendly, open culture and values innovation in AI and data.
Design and deploy ML-based anomaly detection pipelines to flag data discrepancies early in the ETL process.
Build AI-assisted field mapping and classification tooling to accelerate schema mapping across data conversion cycles.
Develop automated data quality scoring pipelines and maintain lightweight AI tooling for staff to operate and extend.
Derex Technologies Inc specializes in providing IT consulting, staffing solutions and software services. Since 1996, the company has delivered customized IT talent solutions to global clients across North America, serving industries like finance, healthcare, and government.
Collaborate with data scientists and software engineers to build scalable data pipelines and ML deployment systems.
Troubleshoot issues across the ML infrastructure stack, from Linux and Docker to Kubernetes and model serving.
Drive high engineering standards through code reviews, testing, and CI/CD enhancements.
Quillbot helps students and professionals strengthen their writing with AI-powered tools. We serve over 56 million users globally and foster a collaborative, virtual-first culture.
Build and ship AI agents, APIs, and applications on Affirm's internal platform, owning the full lifecycle from architecture to production.
Turn messy business requirements from People Operations stakeholders into production systems, integrating with tools like Workday and Notion.
Design reliability infrastructure for multi-model LLM services, including structured output validation and quality controls.
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. The People Tech & Analytics team builds and owns the data, AI, and technology infrastructure for Affirm's People function, running like a product engineering group embedded in HR.
Define and document enterprise AI use cases, business value drivers, and target delivery models aligned with organizational goals.
Develop and maintain current-state and target-state AI architecture across the enterprise, including platforms, data flows, integration patterns, security controls, and governance.
Lead build-versus-buy evaluations for AI platforms and services, establish reusable architecture patterns, and guide proof-of-concept strategies.
ISC2 is the world's leading nonprofit member organization for cybersecurity professionals, dedicated to a safe and secure cyber world. With a globally recognized portfolio of certifications and a charitable arm, the organization fosters an inclusive culture built on integrity, advocacy, commitment, inclusion, and excellence.
Design, develop, and deploy scalable AI solutions using RAG, agentic frameworks, and MLOps.
Collaborate cross-functionally on cutting-edge projects integrating tools like LangChain, CrewAI, and Google ADK.
Apply advanced prompt engineering and monitor AI application performance to ensure robust solutions.
Blend is a premier AI services provider that co-creates meaningful impact for clients through data science, AI, technology, and people. The company fosters a collaborative culture with opportunities for learning and growth across a diverse team.
Design, train, and evaluate machine learning models to address business problems.
Build and maintain data pipelines and infrastructure for model development and deployment.
Deploy ML models into production and monitor performance, reliability, and drift.
Critical Software delivers software solutions and consulting in complex, business-critical environments across industries like aerospace, defense, and healthcare. They are a Benefit Corporation committed to positive impact and an equal opportunity employer.
Lead complex AI engineering workstreams across multiple business areas and stakeholder groups.
Design and build AI-powered applications using LLMs, agents, and modern architectures.
Establish best practices for prompt engineering, API integrations, and code quality.
The partner company focuses on designing and delivering advanced AI solutions that help organizations transform business processes and create measurable value. It offers a collaborative and inclusive culture with opportunities for career growth and international mobility.
Own ML models across their full lifecycle from data pipelines to deployment and monitoring, ensuring reliable performance.
Run and improve the ML platform including GitOps CI/CD, monitoring serving endpoints, and defining SLOs.
Collaborate with risk, operational, and product teams to turn ML into business value across the organization.
Alma provides installment and deferred payment solutions to help merchants boost sales and customer loyalty, without encouraging bad debt. With over 25,000 merchants, 10 million consumers, 380+ employees, and over €100M ARR, they are a Next40 member scaling rapidly across Europe.
Design and deploy AI/ML and Generative AI models to drive operational efficiencies across VBC and RCM datasets.
Implement advanced architectures including LLMs, RAG, and agentic frameworks, and build end-to-end pipelines using Python, PySpark, or T-SQL.
Apply MLOps best practices and work with cloud platforms like GCP to ensure scalable and responsible AI deployments.
Privia Health is a technology-driven national physician enablement company that collaborates with medical groups, health plans, and health systems to optimize physician practices and improve patient experiences. The company is led by top industry talent and focuses on reducing healthcare costs through cloud-based technology and scalable operations.
Partner directly with customers to translate challenges into scalable AI solutions and lead technical discovery.
Design, build, and deploy AI applications using LLMs, cloud-native architectures, and CI/CD pipelines.
Mentor engineers and guide technical workstreams across multiple client engagements with strong ownership.
Jobgether is an AI-powered job matching platform connecting candidates with hiring companies. They use automated processes to evaluate applications and share shortlists with employers, emphasizing efficient, fair recruitment.
Provide technical leadership and mentorship to junior engineers on the Applied AI team.
Design and implement scalable architectures for LLM-based applications, including agents and vector databases.
Develop evaluation pipelines and prototype components to validate AI product concepts.
Jobgether is an AI-powered job matching platform that connects candidates with hiring companies. They prioritize fair and efficient recruitment, fostering an inclusive and diversity-driven workplace culture.
Design and architect AI capabilities on a cutting-edge iPaaS platform, working with technologies like LLM, RAG, Azure AI, and AWS Bedrock.
Build robust, scalable AI systems that run 24/7/365, collaborating with engineers, product management, and operations.
Mentor team members, use data-driven decision-making, and stay current with emerging AI and cloud computing trends.
Jitterbit is a leading data, application, and process workflow automation solution, rooted in iPaaS and fueled by an ambitious vision to integrate critical business processes. The company empowers enterprises of all sizes to accelerate their digital journey and is recognized in Gartner MQ for seven straight years, with a distributed, fun, fast-paced, and performance-oriented culture.
Build full-stack AI/ML-driven HealthTech applications and microservices for clients.
Integrate applications with ML and third-party technologies, and optimize existing systems.
Participate in code reviews, shape technical architecture, and become an SME on owned services.
Serious Development is a boutique healthcare strategy, product, and software engineering firm that partners with healthcare organizations to solve complex operational challenges through custom software. The company fosters a collaborative culture with agile engineering teams, emphasizing high-quality code and team growth.
Design and maintain scalable ML infrastructure including data pipelines, training workflows, and model deployment systems.
Own end-to-end ML lifecycle operations, ensuring reliable delivery of models into production at scale.
Implement monitoring, telemetry, and feedback loops for ML models running across large-scale device fleets.
Our partner company develops ML systems for connected hardware products used by customers worldwide. They operate in a fast-paced, product-driven environment with a collaborative and technically ambitious culture focused on real-world ML impact.
Lead data initiatives using machine learning and AI to optimize ad delivery and platform performance.
Design, build, and maintain scalable AWS data pipelines for ML tooling and high-velocity ad systems.
Productionize forecasting and optimization models with robust backtesting, monitoring, and guardrail systems.
AdsByNimbus is a leader in Publisher-first mobile AdTech. The team is lean, highly capable, and deeply collaborative, offering high levels of project ownership and autonomy.
Lead and develop a high-performing team of MLOps engineers, fostering technical excellence and collaboration.
Define and execute the MLOps roadmap, aligning infrastructure initiatives with research, engineering, and product goals.
Design and maintain scalable ML infrastructure including automated training pipelines, CI/CD, and model serving platforms.
Our partner is a company focused on cutting-edge machine learning infrastructure for large-scale AI systems. They foster an inclusive, mission-driven culture with international collaboration and value innovation, diversity, and continuous learning.
Lead strategic Professional Services engagements, guiding customers through AI-powered software delivery and feature management implementations.
Partner with customers to design and implement safe rollout patterns for AI, including progressive exposure and experimentation directly in their codebase.
Act as a trusted advisor, building reference implementations and integrating LaunchDarkly into AI architectures to ensure safe, observed, and optimized releases.
LaunchDarkly provides a feature management platform that enables developers to release software safely and efficiently. The company is rapidly growing, with a culture that values humility, collaboration, and respect.
Design, build, and maintain scalable machine learning infrastructure on AWS, including training and deployment pipelines.
Develop and deploy ML models for recommendation systems, fraud detection, credit risk, and personalization use cases.
Implement monitoring, logging, and alerting systems to ensure model performance, stability, and reliability in production.
Our partner is a fast-growing, innovation-driven company where machine learning and AI systems directly power large-scale fintech and commerce experiences. They foster a highly dynamic environment with strong emphasis on experimentation, rapid iteration, and measurable business impact.