Lead technical design and architecture for Medium's ML recommendation backend.
Build and scale feature store, recommender systems, and ML Ops.
Mentor engineers and drive cross-functional technical direction.
Medium is an online publishing platform that connects millions of readers with ideas and stories. The company values engineering excellence, collaboration, and a culture of mentorship and continuous improvement.
Design, build, and maintain scalable real-time ML services for ranking and personalization.
Analyze user behavior and run A/B tests to improve product discovery and engagement.
Collaborate with engineers, data specialists, and business stakeholders to drive measurable outcomes.
The company is an AI-driven e-commerce platform that uses machine learning to power smarter search, discovery, and shopping experiences. It maintains a collaborative, remote-first culture that values ownership, inclusion, and meaningful impact.
Lead architecture and technical decision-making across the data lifecycle, from ingestion to consumption by products and AI.
Drive modern engineering approaches through proofs of concept, technology evaluation, and reimagining solutions for scale.
Mentor engineers and build a culture of strong software engineering practices, experimentation, and shared technical ownership.
Applied Systems provides software, data, and intelligent systems that connect agencies and carriers across the insurance lifecycle. With 40+ years in insurtech, the company builds on a culture of learning, experimentation, and shared ownership.
Develop machine learning algorithms for device identification using noisy, unlabeled data.
Design supervised, semi-supervised, and unsupervised ML approaches.
Own projects from research through deployment to real-time services.
This partner company develops a high-volume, real-time identification platform for browsers and devices. It prioritizes engineering excellence and data-driven decisions, with a globally distributed team of specialists.
Own end-to-end ML systems from data and training to evaluation, inference, and deployment.
Build and evolve training, fine-tuning, and inference infrastructure for large models.
Partner with research and application engineering to turn model capabilities into product improvements.
Next Step Systems is an IT recruiting and staffing agency that connects companies with machine learning and technology talent. They specialize in placing candidates in remote and on-site roles across the United States and emphasize a fast-paced, results-oriented culture.
Own, debug, and improve production ML pipelines for accuracy and efficiency.
Design LLM evals, LLM-as-a-judge systems, datasets, and benchmarks to monitor quality.
Run experiments on prompts, models, and pipeline designs to drive improvements into production.
Mimica is an AI-powered process intelligence platform that helps enterprises reclaim time by analyzing workflows and generating AI agents to automate tasks. Backed by $26M in Series B funding and trusted by Fortune 500 companies, the remote-first team spans 16+ countries and values high ownership and transparent communication.
Design, deploy, and optimize large-scale ML systems for retrieval, ranking, and LLM-powered features.
Lead ML projects from concept to production, improving answer relevance and user experience.
Collaborate with Product, Data Science, and Engineering teams to shape technical direction.
The hiring partner is a technology company focused on building advanced AI systems that help users discover relevant answers from large collections of online conversations. It values innovation, collaboration, and responsible AI development, offering a remote work environment within the United States.
Manage and guide engineering teams, building end-to-end data pipelines using Fivetran, dbt, Snowflake, and Databricks.
Partner with Finance, Sales, and Operations to unify reporting models for pipeline health, ARR, churn, and LTV.
Enforce data governance and modeling standards, while serving as a trusted advisor to C-suite leaders.
JumpCloud is the AI-powered unified IT management platform that secures the modern workforce by consolidating identity, device, and access management. It is a remote-first company with teams in over 15 countries, fostering a collaborative and inclusive culture.
Develop, train, and optimize machine learning models using Python and common ML libraries.
Support data preprocessing, feature engineering, and CI/CD integration for scalable ML solutions.
Collaborate with data engineers and stakeholders in an Agile environment to align ML solutions with business needs.
Makpar is a professional and technical solutions provider for the Federal government, specializing in cloud engineering, data management, cybersecurity, and emerging technologies. The company's connected and engaged workforce is dedicated to delivering success for clients and the American people.
Collaborate with Data Science teams to deploy and maintain machine learning models in production environments.
Build robust data processing pipelines and implement MLOps and LLMOps best practices for scalable AI solutions.
Work with cloud technologies, big data platforms, and machine learning frameworks to deliver innovative technical solutions.
The company specializes in data and technology services, helping transform machine learning solutions into production-ready systems. It is an established international organization with a collaborative, inclusive culture and a strong emphasis on continuous learning and career growth.
Lead the end-to-end machine learning lifecycle, from data preparation and model training to evaluation, inference, and deployment.
Build and optimize scalable training, fine-tuning, and inference pipelines for large-scale models on GPU infrastructure.
Establish production best practices for model quality, reliability, safety, and measurable performance improvements.
This company builds production-grade machine learning systems and large-scale GPU-powered inference pipelines. It is a fully remote team that values technical rigor, collaboration, and hands-on engineering.
Develop deep understanding of customer problems and map them to Cohere solutions as a trusted technical advisor.
Lead design and deployment of customer pilots, owning the technical narrative from discovery to business case.
Collaborate with Product and Engineering teams and partners to scale solutions and gather customer feedback.
Cohere is a leading security-first enterprise AI company building cutting-edge foundation AI models and products for real-world business problems. It is a global team of researchers, engineers, and designers headquartered in Toronto with offices worldwide, committed to driving AI adoption.
Build and operate production ML systems for recommendations, ranking, and personalized action selection across Stripe's Growth Platform.
Improve contextual bandit and policy-learning approaches, including exploration, reward design, and adaptive user feedback.
Develop reliable data and feature pipelines, reusable tooling, and online experiments to drive measurable business outcomes.
Stripe is a financial infrastructure platform for businesses of all sizes, enabling payments and revenue growth. With millions of companies as customers, Stripe fosters a culture of passion, grit, and integrity while working to increase the GDP of the internet.
Lead the team in roadmapping and executing machine learning features and products.
Improve team pace through technical expertise and pragmatic tradeoffs.
Drive recruiting, reliability, and growth of team members.
Hightouch helps companies sync data into their SaaS systems to automate and improve operations. They are a remote-first company with hundreds of customers and a culture focused on engineering excellence.
Design and own scalable data infrastructure for real-time and batch processing, including pipelines handling billions of events daily.
Architect medallion-layer data models in Databricks and build AI-assisted workflows with Claude Code.
Partner with analytics and data science teams to deliver reliable datasets and enable natural-language querying.
Life360 is a family safety and location-sharing platform offering mobile apps, Tile trackers, and Pet GPS with crash detection. It has 102M+ monthly active users and 500+ remote-first employees, with an AI-native, trust-based culture.
Architect, implement, and maintain foundational ML infrastructure powering Feeds Ranking, Content Understanding, Recommendations, and Ads.
Create ML data-governance tools that help MLEs discover and reason about signals.
Drive complex projects end to end from technical planning through implementation, testing, rollout, and ongoing operation.
Reddit is a community of communities hosting authentic conversations on shared interests. With 100,000+ active communities and about 130 million daily active visitors, it is a top internet information source.
Own the full ML lifecycle for Misdirected Email detection, from data wrangling to deployment and monitoring.
Run rigorous experiments and A/B tests, set thresholds, and conduct error analysis to prevent regressions.
Partner with product and engineering stakeholders to align deliverables and ensure successful GA launches.
Abnormal protects the humans behind the world's most critical organizations from AI-powered cybercrime. 5,000 enterprises trust our behavioral AI platform.
Design, build, and productionize machine learning and agentic AI functionality.
Partner with product engineering squads to embed AI capabilities into Arkestro's product.
Contribute to AI/ML architecture decisions and own evals and ground truth for your projects.
Arkestro's Predictive Procurement Platform applies AI, game theory, and behavioral science to enterprise negotiations, helping teams move from reactive supplier bidding to data-driven offers. It is a hyper-growth, remote-first startup with a collaborative culture focused on meaningful work and significant impact.
Build data pipelines that pull information from ERP, CRM, and accounting systems, covering ingestion, processing, and storage.
Put data quality and lineage controls in place so any number can be traced back to its source.
Build and manage training and evaluation datasets that power and measure AI models.
Lunon is an AI-native consulting firm in New York City backed by General Catalyst, embedding engineers inside client businesses to ship AI solutions that run without them. The team is young and fast-moving, with everyone owning what they ship end to end.
Develop and deploy statistical and machine learning solutions for a major U.S.-based e-commerce retailer.
Collaborate with project leadership, data specialists, and engineering teams to translate business challenges into analytical models.
Drive end-to-end data science projects from data exploration and feature engineering through predictive modeling and production deployment.
The company is a technology consultancy that delivers data science and machine learning solutions to clients. It is a growing, fully remote organization that values continuous learning and professional growth.