Role Description Join a leading AI lab's cutting-edge Generative AI team and play a key role in developing next-generation large language models. We are seeking experienced MLOps and ML Systems Engineers with deep expertise in PyTorch and kernel-level programming frameworks such as Triton or Pallas. This is a full-time (40 hours/week) engagement supporting high-impact AI research and engineering efforts. - Partner with research and engineering teams to identify and address knowledge gaps in MLOps, machine learning infrastructure, and model training systems. - Design challenging, real-world tasks focused on distributed training, ML frameworks, model optimization, and infrastructure engineering. - Develop accurate, well-structured solutions to complex MLOps and ML systems problems. - Evaluate technical tasks and solutions, providing detailed and actionable feedback. - Create evaluation frameworks and scoring rubrics for training pipeline architecture, distributed systems reasoning, performance optimization, and kernel-level programming. - Contribute domain expertise to improve AI model capabilities in machine learning engineering topics. - Collaborate with other subject matter experts to ensure consistency, quality, and technical accuracy across datasets and evaluations. Qualifications - 2+ years of professional experience in ML Infrastructure, MLOps, ML Systems Engineering, or a closely related field. - Strong hands-on experience building and operating production-scale machine learning systems. - Advanced proficiency with PyTorch, including model training, optimization, and deployment workflows. - Experience developing, tuning, or optimizing custom GPU kernels using Triton, Pallas, or similar frameworks. - Demonstrated career growth and increasing technical responsibility. - Ability to commit to a full-time, 40-hour-per-week schedule during standard business days. - Excellent written communication skills and the ability to clearly explain complex technical concepts and engineering decisions. Requirements - Experience with large-scale distributed training frameworks and infrastructure. - Knowledge of GPU performance optimization and compiler-level ML tooling. - Familiarity with modern AI training pipelines, model evaluation methodologies, and LLM development workflows. - Experience mentoring engineers or contributing to technical standards and best practices. - Background in cloud-native ML infrastructure and production deployment environments. Benefits - Work alongside leading AI researchers and engineers on frontier AI systems. - Influence the development and evaluation of next-generation large language models. - Apply your expertise to solve challenging machine learning infrastructure and optimization problems. - Contribute to high-impact projects at the forefront of AI innovation. Additional Information - Full-time engagement requiring 40 hours per week. - Dedicated commitment is expected during the engagement period. - Responsibilities and project scope may evolve based on research priorities and business needs. Equal Opportunity Statement All qualified applicants will be considered without regard to legally protected characteristics. Reasonable accommodations are available upon request.

Role Description This role is for one of our clients. Our research initiative focused on training AI agents to handle complex, real-world business and consulting work. As a contributor, you'll help design and refine the scenarios used to evaluate and improve how AI systems approach multi-document, multi-stakeholder business problems. This is a long-term role with flexible hours and a consistent workload. - Conduct market research and competitive analysis - Develop business cases, operational frameworks, and go-to-market strategies - Synthesize data into insights using presentations, memos, and models - Collaborate with stakeholders to define key metrics and decision points - Support strategic planning, scenario modeling, and opportunity evaluation Qualifications - 1.5+ years at a top management consultancy (e.g., McKinsey, BCG, Bain, or Big 5) - Strong analytical and communication skills, both written and verbal - Comfortable working independently in ambiguous or rapidly evolving contexts - Proficiency in PowerPoint, Excel, and basic data analysis tools - Experience advising on tech, AI, or SaaS industries preferred but not required Requirements - Expected commitment: minimum 10 hours/week Application Process - Submit your resume to get started - You'll complete a short form to detail your consulting background - We'll follow up within a few days with potential next steps Contract and Payment Terms - You will be engaged as an independent contractor. - This is a fully remote role that can be completed on your own schedule. - Projects can be extended, shortened, or concluded early depending on needs and performance. - Your work will not involve access to confidential or proprietary information from any employer, client, or institution. - Payments are weekly on Stripe or Wise based on services rendered. - Please note: We are unable to support H1-B or STEM OPT candidates at this time.

Role Description This role is for one of our clients. Our research initiative focused on training AI agents to handle complex, real-world business and consulting work. As a contributor, you'll help design and refine the scenarios used to evaluate and improve how AI systems approach multi-document, multi-stakeholder business problems. This is a long-term role with flexible hours and a consistent workload. - Conduct market research and competitive analysis - Develop business cases, operational frameworks, and go-to-market strategies - Synthesize data into insights using presentations, memos, and models - Collaborate with stakeholders to define key metrics and decision points - Support strategic planning, scenario modeling, and opportunity evaluation Qualifications - 1.5+ years at a top management consultancy (e.g., McKinsey, BCG, Bain, or Big 5) - Strong analytical and communication skills, both written and verbal - Comfortable working independently in ambiguous or rapidly evolving contexts - Proficiency in PowerPoint, Excel, and basic data analysis tools - Experience advising on tech, AI, or SaaS industries preferred but not required Requirements - Expected commitment: minimum 10 hours/week Application Process - Submit your resume to get started - You'll complete a short form to detail your consulting background - We'll follow up within a few days with potential next steps Contract and Payment Terms - You will be engaged as an independent contractor. - This is a fully remote role that can be completed on your own schedule. - Projects can be extended, shortened, or concluded early depending on needs and performance. - Your work will not involve access to confidential or proprietary information from any employer, client, or institution. - Payments are weekly on Stripe or Wise based on services rendered. - Please note: We are unable to support H1-B or STEM OPT candidates at this time.

Title: Forward Deployed AI Engineer - Enterprise Lead Location San Francisco; New York Employment Type Full time Location Type Hybrid Department Engineering Job Description: About Distyl AI Distyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations. We research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys. Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business. What We Are Looking For Enterprise Lead AI Engineers are senior technical leaders accountable for the technical success of Distyl’s most complex enterprise AI deployments. They may own one large customer engagement or provide technical oversight across multiple smaller accounts, maintaining technical coherence across workstreams, developing technical leaders, and ensuring production AI systems deliver durable business value in complex customer environments. This role is for leaders who can operate across altitudes: reasoning with customer executives about tradeoffs and delivery risk, setting architecture across multiple teams, developing Forward Deployed AI Engineer Leads and senior engineers, and staying close enough to the work to make sound technical judgments about AI system behavior, code quality, evaluations, integrations, and production reliability. Key Responsibilities - Lead the technical success of complex enterprise AI deployments, either across one large customer engagement or a portfolio of smaller accounts, often spanning multiple use cases, production systems, workstreams, and teams - Set technical direction across engagements, ensuring architecture, system behavior, evaluation strategy, integrations, and operational practices remain coherent as deployments grow - Develop Forward Deployed AI Engineer Leads, senior ICs, and emerging technical leaders, raising the bar for technical judgment, code quality, customer communication, and production ownership - Serve as the senior technical partner to customer executives, enterprise architects, and business partners, building trust through clear communication, sound judgment, and reliable delivery - Maintain technical proximity to live systems by reviewing designs, interrogating system behavior, debugging critical issues, guiding evaluation strategy, and participating in incident reviews and production quality improvements - Use AI-native engineering practices to increase your own leverage and model effective use of AI tools for design, implementation, debugging, analysis, experimentation, and operational improvement - Partner with company leadership on staffing, organizational design, leadership development, and customer expansion as engagements grow from pilot to enterprise-scale deployment What We Require - 8+ years of software engineering experience, including experience leading technical teams through ambiguous, high-stakes production delivery - Multi-team technical leadership. You have led technical execution across multiple teams, workstreams, accounts, or senior engineers. You know how to create clarity, set standards, develop leaders, and keep teams aligned without becoming a bottleneck - Talent development. You raise the capability of the engineers around you. You can coach Tech Leads, senior ICs, and emerging leaders, and you know how to build a talent-dense engineering team over time. Prior people management experience is strongly preferred, but not required if you have demonstrated organizational leadership across multi-team engineering efforts - Enterprise technical ownership. You take responsibility for whether complex technical deployments deliver durable customer value. You can reason across architecture, delivery risk, team capability, customer constraints, system behavior, and production operations - Technical proximity and judgment. You can still reason deeply about production software. You understand architecture, debugging, testing, observability, performance, maintainability, security, and code quality. While you mostly operate through your teams, you are willing and able to do selective, high-leverage technical work when it materially improves outcomes - Customer and executive judgment. You can operate effectively in high-trust, high-visibility customer environments. You communicate clearly with executives, IT leaders, data teams, and operational SMEs about system behavior, delivery tradeoffs, risks, and limitations - AI-native working style. You use AI tools daily to write and debug code, explore designs, analyze data, automate repetitive work, and improve your own leverage. You are curious about new model capabilities and actively incorporate them into how you lead and build - Willingness to travel. Travel is typically 20–40%, depending on the project, customer needs, and your role on the engagement What We Offer - The base salary range for this role is $180K – $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package - 100% covered medical, dental, and vision for employees and dependents - 401(k) with additional perks (e.g., commuter benefits, in‑office lunch) - Access to state‑of‑the‑art models, generous usage of modern AI tools, and real‑world business problems - Ownership of high‑impact projects across top enterprises - A mission‑driven, fast‑moving culture that prizes curiosity, pragmatism, and excellence Distyl has offices in San Francisco and New York. This role follows a hybrid collaboration model with 3+ days per week (Tuesday–Thursday) in‑office. #LI-Hybrid We believe diverse perspectives make our work stronger and more impactful. We are an equal opportunity employer and evaluate all applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other legally protected characteristic. We encourage candidates from all backgrounds to apply.