The Staff AI/ML Engineer guides portfolio companies on AI product development, creates prototypes, experiments with AI tools, and advises on best practices.
Who We Are: Cox Exponential (CX2) is a unique take on a venture fund, started by a group of former ML researchers and technical cofounders who are passionate about the nuts and bolts of company building. We’re a cross between an early-stage investment firm, a technical accelerator, and an incubator. We don’t just write checks; we actively partner with founders to help grow their team and product vision.
If you love the energy of startups and the flexibility of academia, but also value financial stability and work/life balance, consider joining us at CX2!
We are looking for a creative, driven engineer with a background in AI and Machine Learning, who can provide technical guidance and support to our portfolio companies. In this role, you will help founders vet and steer the use of AI in their products, advise on best practices and common pitfalls, run experiments using a variety of methods and tools, and partner with your peers to our engineering organization to develop end-to-end prototypes of AI-powered features. While the focus will often be on modern language-based and agentic systems, you are encouraged to draw from a broader ML repertoire: The simplest solutions are often the best!
As Staff AI/ML Engineer, you will report directly to a Managing Partner at CX2, and be responsible for:
- Leveraging deep experience in AI and ML to help steer our portfolio company’s product roadmaps
- Experimenting with a variety of AI/ML tools to gauge their efficacy at solving specific, practical problems
- Partnering with a team of engineers (both within CX2 and our portfolio companies) to develop full, end-to-end prototypes of AI-powered features
- Acting as a sounding board for founders and their teams as they build AI products, including advising against common pitfalls
- Keeping up-to-date on the latest advances in GenAI, with a particular focus on Large Language Models, Agentic Systems, and related concepts (vector search, fine-tuning, prompt engineering, orchestration systems, etc)
- Aiding in the technical due diligence process, when appropriate, by vetting the feasibility and defensibility of prospective companies’ ML-powered products
- 5+ years of experience shipping AI/ML-powered products to real end users; the more variety, the better!
- 2+ years of specific experience building working products which leverage modern Generative AI techniques, with an emphasis on LLMs and agentic systems
- A rich understanding of the pragmatic aspects of working with AI in the modern era: prompt engineering, multi-agent verification systems, RAG, trade-offs in performance vs compute costs, etc.
- A background in more traditional Machine Learning techniques, including hands-on experience in training, evaluating, and deploying custom models for bespoke tasks
- Fluency in two or more programming languages, and comfort in learning new ones as the technical stack demands
- Excellent communication skills, and a willingness to work directly with clients
- Flexibility, enthusiasm, and the confidence to tackle challenges outside of your comfort zone
- Transparency; comfort with asking questions, voicing gaps in your understanding, and accepting when a particularly promising approach hits a dead end – they often do!
- A Bachelors or higher in Computer Science or another software engineering-related field
- A Masters or higher in an AI/ML adjacent field, or demonstrable experience in conducting open-ended AI/ML research in a non-academic environment
- Experience in a role which saw you juggling multiple research problems in parallel. (Examples: Technical consulting in an AI/ML related field; an Architect role at a large company; directing a team of AI/ML researchers in a corporate environment; mentoring multiple students)
- Experience in a high-paced startup environment
- Fluency in lower level languages and optimization (C/C++, OpenGL, Cuda)
- A passion for, and history of, open source development
- Demonstrated experience in communicating technical concepts to a nontechnical audience (teaching, blogging, public speaking, etc)
Top Skills
AI
C/C++
Cuda
Generative Ai
Large Language Models
Machine Learning
Opengl
Similar Jobs
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.
Top Skills:
Machine LearningPythonPyTorch
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
As a Staff ML Infra Engineer, you will develop and deploy offboard machine learning solutions for autonomous vehicles, ensuring model integration and performance across teams. You'll build ML infrastructure, implement CI/CD pipelines, support data curation, and mentor engineers.
Top Skills:
Ci/CdDockerKubernetesNumpyPythonPyTorch
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
Lead the design, implementation, and deployment of scalable ML infrastructure for autonomous driving. Drive technical projects, mentor others, and collaborate with teams to enhance ML workflows.
Top Skills:
C++DockerKubernetesMlopsPythonPyTorchTensorFlow
What you need to know about the Colorado Tech Scene
With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.
Key Facts About Colorado Tech
- Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
- Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
- Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
- Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
- Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute
