Transform research-grade ML code into production-ready, scalable software systems.
Take ownership of end-to-end ML deployment pipelines including data ingestion, feature engineering, and monitoring.
Work closely with researchers and engineers to translate research objectives into robust production solutions.
Our partner is a company in the AI/ML and financial technology space, operating with a distributed international team. They foster a collaborative, entrepreneurial culture with a focus on innovation and high-impact work.
Build and maintain automated, tested ML/data pipelines to take data science models from notebooks to production.
Optimize CI/CD, workflow orchestration, and observability to keep match data reliable at scale.
Partner with data science to maintain feature pipelines, run EDA, and apply AI tools across workflows.
PAR Technology is a leader in restaurant technology, providing software and hardware that help brands create guest connections and drive growth. Serving over 100,000 restaurants in 110+ countries, it fosters a collaborative 'Better Together' culture within its growing SaaS organization.
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.
Bridge data science and production by building MLOps pipelines.
Develop scalable data processing solutions with Spark and Databricks.
Productionize ML models and ensure reliability through monitoring.
This company powers the global digital audio advertising ecosystem, turning large-scale listener data into actionable insights for advertisers. It operates in a technically advanced, cross-functional environment with a culture centered on respect, equity, and professional development.
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.
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.
Build production-ready infrastructure for autonomous AI agents, including secure code execution, state management, and model routing.
Collaborate with research teams to turn emerging ML ideas into scalable, fault-tolerant distributed systems.
Prototype and evaluate new technologies for agent workflows, from identity to resource management.
Cohere is a security-first enterprise AI company building foundation models and end-to-end products for real-world problems. We are a global team of engineers, researchers, and designers passionate about our craft, with offices worldwide.
Own ML projects end-to-end, from exploration to production deployment, iterating based on real user feedback.
Build agentic LLM systems with multi-step workflows, tool use, retrieval, and orchestration for reliable clinical use.
Treat evaluation as core engineering work, building eval sets, LLM-as-judge pipelines, and regression tests to catch quality drops.
Sword is building AI to revolutionize healthcare, focusing on clinical reasoning and real-time treatment. With over 700,000 members and 1,000+ enterprise clients, they are backed by $500M+ from top investors like Khosla Ventures and General Catalyst, defining a new standard in AI-native care.