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.
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.
Build and operate the real-time inference service for the risk decision engine with low latency and high availability.
Own model deployment infrastructure including CI/CD, shadow mode, and staged rollouts.
Build model observability and partner with Risk Data Science for production operation.
Mercury is a fintech company that provides banking services for startups via partner banks. The company is committed to creating a safe environment and values diversity, with a growing team focused on innovation.
Drive end-to-end ML development for customer-facing SaaS products, from pipelines to production deployment and monitoring.
Design evaluation strategies and A/B tests to prove ML features improve customer outcomes and business impact.
Influence product roadmap by communicating ML capabilities and trade-offs to cross-functional teams.
WorkWave provides field service and logistics software solutions that help businesses manage their operations and serve their customers. They are a global company with a remote-first culture, recognized as a Best Place to Work and named among the top software companies worldwide.
Design and operate core AI platform components for training, deploying, and serving ML models at scale.
Own model serving and inference workflows end-to-end, optimizing for reliability, latency, throughput, and cost.
Collaborate with product, infrastructure, and security teams to build scalable platform capabilities for AI-powered features.
Mozilla Corporation is the non-profit-backed technology company behind Firefox and Pocket, with over 225 million monthly users. A wholly-owned subsidiary of the Mozilla Foundation, the company is mission-driven, employee-owned, and focused on privacy and open standards.
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.
Design and build scalable ML training, deployment, and inference pipelines using CI/CD and cloud infrastructure.
Implement MLOps for model versioning, monitoring, and automated retraining to detect drift and performance degradation.
Partner with Data Scientists and Product teams to productionise models and integrate ML into customer-facing products.
We develop solutions that make an impact for companies around the globe. Our culture embraces openness, acts with respect, shows grit & guts, and combines employment with enjoyment.
Build and operate the ML lifecycle platform, including tooling for experiment tracking, model registry, and versioned pipelines.
Own CI/CD and deployment for ML workloads, building automated pipelines from notebook to production.
Make models observable and reliable in production with monitoring for latency, drift, data quality, and cost signals.
dv01 provides a data analytics platform for the structured finance market, offering transparency into investment performance and risk for lenders and Wall Street investors. With over 400 clients and coverage of over 100 million loans, dv01 is a data-first company with a diverse and innovative culture.
Assess current pipelines and data architecture to produce a prioritized plan for change.
Design durable data and ML systems grounded in customer needs with documented tradeoffs.
Harden pipelines, upgrade data architecture, and raise standards for observability and reliability.
FutureFit AI's core mission is to help more people get to better jobs faster and cheaper, with a focus on those facing barriers to opportunity. Their team of 30-50 across the US and Canada fosters a high trust, high intensity culture with a will to win.
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, ship, and own product features end-to-end using cutting-edge AI/ML techniques.
Apply classical ML and LLM-based approaches like RAG, prompt engineering, and fine-tuning to enhance the audit and risk platform.
Collaborate with cross-functional teams in an Agile environment to deliver scalable, production-quality code.
Optro is a leading audit, risk, ESG, and InfoSec platform trusted by over 50% of the Fortune 500. The company has been named one of the 500 fastest-growing tech companies in North America for seven consecutive years, fostering a culture of innovation and collaboration.
Lead the ML strategy for Workforce Management, delivering production systems for forecasting, routing, and optimisation.
Coach and develop a growing team of Machine Learning Scientists embedded in product squads.
Work cross-functionally with Product, Engineering, Data, and Operations leaders to solve ambiguous operational problems.
Monzo is a digital bank on a mission to make money work for everyone, offering personal and business bank accounts, savings, investments, and pension consolidation. With over 10 years of growth in the UK and a focus on financial education and award-winning customer service, Monzo has a vibrant culture and a strong commitment to diversity and inclusion.
Own the ML serving API and deploy models to production with CI/CD and infrastructure as code.
Build monitoring, alerting, and reliability for NBA models and LLM agents.
Drive architectural decisions and mentor engineers on MLOps patterns.
Clutch is a vertical SaaS company backed by Andreessen Horowitz, revolutionizing how credit unions engage with members via fintech lending software. The company is small and ambitious, with a lean data team of five that values pragmatism and fast shipping.
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.
Design, build, and deploy AI/ML solutions from prototype to production for client business problems.
Apply generative AI and LLMs, establishing MLOps best practices including CI/CD and model monitoring.
Serve as a trusted technical advisor, translating ambiguous problems into well-scoped solutions and presenting to stakeholders.
DevIQ builds modern cloud and data solutions for mid-market companies focused on energy reduction, healthcare, education, and smart cities. The company offers competitive benefits, a strong team culture, and opportunities to work on end-to-end solutions with multi-disciplinary teams.
Design, build, and scale systems for customer-facing data products and machine learning workloads.
Collaborate with product, engineering, and cross-functional partners on complex data challenges.
Implement data quality, observability, and governance frameworks for reliable data at scale.
Boulevard provides the first and only client experience platform for appointment-based self-care businesses. The company has an insatiable curiosity and embraces experimentation, with a diverse team that values open communication and equal opportunity.
Build and ship ML models to help customers search and find answers about their financial lives.
Develop user and product embeddings, contextual bandits, and personalized ranking algorithms.
Work with a modern cloud-native data platform to deploy models to millions of customers.
Monzo is on a mission to make money work for everyone, offering personal and business bank accounts, savings, investments, and pensions with a focus on solving problems and changing lives. With over 15 million customers and a culture of innovation, Monzo prioritizes diversity and inclusion.
Design and build systems that improve the efficiency of ML training and inference workloads.
Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance.
Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements.
Reddit is a community of communities built on shared interests, passion, and trust, hosting the most open and authentic conversations on the internet. With over 100,000 active communities and approximately 126 million daily active users, Reddit is one of the internet's largest sources of information.
Build ML infrastructure for low-latency model deployment, distributed inference pipelines, and real-time telemetry.
Scale ranking systems by moving models from experimentation to production, optimizing latency and cost trade-offs.
Implement model CI/CD for automated versioning, canary releases, hot-swappable container rollouts, and zero-downtime rollbacks.
Sequen provides an integrated platform that pairs cutting-edge frontier ranking models with infrastructure to run them in production at sub-10ms latency and enterprise scale. They are a small, highly technical, early-stage team focused on turning recent AI advances into production-grade systems.
Evolve and maintain our Kubeflow, Feast, and Spark-on-Kubernetes ML infrastructure.
Design tools and APIs empowering teams to transition from centralized bottlenecks to self-service excellence.
Collaborate with Data Science teams to apply software engineering best practices to ML workflows.
Wellhub revolutionizes workplace wellness by connecting employees to partners for fitness, mindfulness, therapy, nutrition, and sleep in one subscription. Headquartered in NYC with team members across the globe, we value wellbeing, collaboration, and different perspectives.