Lead technical discovery with foundation model labs, frontier AI teams, and large enterprises to understand model objectives and constraints.
Design end-to-end solutions across the post-training stack including SFT data curation, RLHF/DPO pipelines, custom benchmarks, and LLM-as-judge systems.
Author technical proposals, run workshops and POCs, and serve as ongoing technical advisor during delivery.
Own the design and defense of frontier model evaluations across reasoning, coding, agents, tool use, and multi-modal.
Build benchmark packages with expert-verified ground truth, multi-model headroom results, and rigorous QC.
Recruit, calibrate, and review a pool of subject-matter experts in coding, agentic/tool-use, and STEM/reasoning.
Anyone AI measures frontier model capability through expert-verified evaluation packages. The company operates as a remote team with a focus on rigorous benchmarking and lab collaboration.
Build, train, and improve machine-learning models for production use.
Develop evaluation and feedback loops that measurably improve model performance over time.
Evaluate new models and techniques, bringing the best into production.
Allocate transforms private market investing by enabling RIAs and family offices to discover, model, and manage private market exposure. The company provides a platform with curated fund and co-investment opportunities, and values meritocracy, civil discourse, and continuous improvement.
Design and implement multi-agent AI systems using frameworks like LangChain and CrewAI, building agent-to-agent orchestration pipelines.
Fine-tune foundation models, integrate retrieval-augmented generation, and develop APIs and backend services for production deployment.
Containerize and deploy agents with Docker and Kubernetes, while collaborating with QA and product teams to benchmark accuracy and safety.
Innodata is a global data engineering company focused on enabling the responsible advancement of artificial intelligence by providing data, evaluation frameworks, and human expertise. With over 36 years of experience, the company delivers high-quality data solutions and services for Generative AI builders and adopters.
Develop and fine-tune large language models for intelligent, safe, and responsive browser interactions.
Apply retrieval-augmented generation, summarization, classification, and intent modeling in real-world browser workflows.
Collaborate with product and engineering partners to design, iterate, and launch user-facing AI features aligned with Mozilla's values.
Mozilla Corporation is a non-profit-backed technology company that makes Firefox and Pocket, with a mission to reclaim an internet built for people. With over 225 million monthly users and a focus on AI, security, and open-source software, Mozillians work in a collaborative, mission-driven culture.
Own end-to-end Machine Learning (ML) system execution including data pipelines, training, and deployment.
Fine-tune and adapt models using state-of-the-art methods like LoRA and DPO.
Architect scalable inference systems and collaborate closely with application engineering.
This company develops advanced production-grade machine learning systems. The team is small and high-trust, with a culture of ownership and pragmatism.
Design, train, evaluate, and ship ML systems for governance and security, starting with prompt injection detection and behavioral anomaly detection.
Build supporting infrastructure including data pipelines, feature stores, model serving, and evaluation harnesses.
Set technical direction for ML work, own architecture, evaluation methodology, and model lifecycle.
Docker provides developer tools for building, sharing, and running applications across Docker Desktop, Docker Hub, and Docker Scout. With over 20 million monthly users and a globally distributed remote-first team, Docker is trusted by solo founders to the world's largest companies.
Partner with enterprise clients to understand challenges and design robust technical solutions, from APIs to AI workflows.
Own production deployments ensuring reliability, observability, and continuous improvement of delivered systems.
Build AI-enabled systems using LLMs and agentic workflows while collaborating across Product, Engineering, and Sales teams.
Our partner is a company focused on deploying AI and enterprise integrations at scale. They operate in a remote-first, high-trust environment with a focus on practical AI deployment.
Design and own the datasets and evaluation specifications for financial-domain LLMs, vision-language models, and AI agents, focusing on unstructured and multimodal financial data.
Translate customer goals into concrete dataset specifications, taxonomies, rubrics, and acceptance criteria, ensuring domain validity and statistical defensibility.
Develop evaluation methodology beyond surface accuracy, covering numerical consistency, hallucination rates, refusal appropriateness, and fairness across customer segments.
Innodata is a global data engineering company that enables the responsible advancement of artificial intelligence by providing data, evaluation frameworks, and human expertise. With over 36 years of experience, the company delivers high-quality data and solutions for Generative AI builders and adopters.
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.
Serve as primary technical advisor to customers, leading discovery sessions and translating business priorities into AI-enabled solutions.
Lead cross-functional engineering teams to design and deploy secure, scalable, and high-performing AI software systems.
Mentor engineers, establish engineering standards, and drive delivery of enterprise-scale AI solutions.
LTS delivers AI-powered software solutions for complex business and mission challenges. The company values innovation, growth, collaboration, and quality, and offers a culture that rewards ambition and performance.
Design, develop, and deploy production-grade AI-powered backend systems integrating LLMs and traditional ML models.
Integrate and optimize vector databases for RAG pipelines, and write clean, well-structured Python code.
Debug complex cross-layer issues and collaborate with product and engineering teams to deliver cohesive solutions.
We are a fast-growing product company integrating cutting-edge AI capabilities into our core offering to deliver exceptional value to customers. Our small, fast-moving team works on practical, real-world AI applications with high autonomy.
Build and improve ML components across data, training, evaluation, and inference.
Implement evaluation and testing to understand model behavior.
Debug model issues, performance problems, and production incidents.
This company builds core ML components for large-scale production systems. They emphasize real-world learning, iteration, and collaboration with senior engineers.
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 and deliver production AI and agentic systems across document intelligence, workflow automation, and copilots.
Own architecture decisions for LLM-based systems, including retrieval, tool use, orchestration, memory, and evaluation.
Manage evals and observability for production AI, ensuring system accuracy and detecting regressions.
Maxwell is a mortgage technology and fulfillment company on a mission to make lending simpler, faster, and more accessible. It is a remote-first team that takes craft seriously and moves with intention, building a cutting-edge AI company in mortgage technology.
Architect and maintain production high-traffic LLM serving systems.
Optimize throughput, latency, and cost for leading open-source LLMs.
Debug and optimize major inference engines like SGLang, vLLM, or TensorRT using PyTorch and CUDA.
We are building decentralized and confidential machine learning infrastructure to enable user-owned AI. Our team is focused on highly scalable and efficient infrastructure for open-source AI at a global scale, with a culture that values innovation and performance.
Build agent harnesses in Python using LangChain and LangGraph, including tool-calling and structured outputs.
Develop evaluation frameworks with golden datasets, LLM-as-judge, and regression suites, wiring them into CI.
Collaborate with data engineers on Snowflake-backed retrieval patterns like Cortex Analyst and Search Services.
Kobie is a loyalty solutions partner that helps brands build emotional connections with their consumers. Named a Top Workplace in the USA, they offer a flexible remote culture with a focus on collaboration and growth.
Develop and improve NLP systems and language model-powered experiences.
Fine-tune and optimize language models for domain-specific use cases and build evaluation frameworks.
Deploy and maintain production-grade ML systems on GPU infrastructure with a focus on scalability and safety.
BetterHelp removes barriers to therapy and makes mental health care accessible globally. Founded in 2013, it is now the world's largest online therapy service with over 30,000 licensed therapists, and it invests deeply in employee well-being and professional development.
Drive revenue growth by leading technical sales strategies and scaling a global solutions architecture team.
Architect scalable, secure AI solutions for enterprise customers and shape product development.
Establish best practices for agentic AI, model customization, and enterprise deployment.
Cohere is the leading security-first enterprise AI company building cutting-edge foundation AI models and end-to-end products. We are a global technology company co-headquartered in Toronto and San Francisco with a passionate team of researchers, engineers, and designers.
Optimize production LLM serving with vLLM and SGLang to maximize throughput and minimize latency through batching and quantization.
Profile training runs to find bottlenecks and resolve them with attention implementations like FlashAttention on H200 and GB200 hardware.
Deploy and operate multiple models on shared GPU clusters with autoscaling, bin-packing, and efficient handling of mixed workloads.
Egen is a fast-growing technology company with a data-first mindset, partnering with clients on Google Cloud and Salesforce to drive action through data and insights. We are a team of dedicated engineers who thrive on solving tough problems and continually innovate to achieve fast, effective results.