AI/ML Engineer

We are Hiring: AI/ML Engineer

This is a fully remote position. Work from anywhere, collaborate across time zones, and ship from wherever you do your best thinking.

We're looking for an AI/ML Engineer who lives at the intersection of model development, infrastructure, and scrappy problem-solving. You'll architect and train AI models inside our private cloud, wire them up to leading generative systems, and build intelligent agents that actually move the needle. This role is for someone who loves moving fast, thinking on their feet, and shaping solutions in real time with a small, high-velocity team.

What You'll Do

Design, train, fine-tune, and deploy ML and deep learning models within our private cloud infrastructure with a focus on security, scalability, and performance. Build integrations between proprietary models and generative AI platforms (OpenAI, Anthropic, open-source alternatives) to extend capabilities and unlock new use cases. Architect and develop AI agents, including multi-agent systems, tool-using agents, and RAG-based assistants that can reason, plan, and act autonomously across workflows. Ingest, clean, and synthesize data from diverse sources, turning raw information into structured signals that drive model performance and business insight. Partner directly with product, engineering, and business stakeholders to scope problems, propose approaches, and iterate on solutions during live working sessions. Establish MLOps practices for the private cloud environment, including model versioning, monitoring, evaluation pipelines, and responsible AI guardrails. Stay ahead of the rapidly evolving AI landscape and proactively bring new techniques, tools, and ideas to the team.

What We're Looking For

A strong foundation in machine learning, deep learning, and modern NLP, with hands-on experience training and deploying models in production. Proficiency in Python and core ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, and LangChain or LlamaIndex. Direct experience standing up ML workloads in private or hybrid cloud environments, including GPU provisioning, containerization (Docker, Kubernetes), and orchestration of training and inference pipelines. Working knowledge of generative AI patterns: prompt engineering, fine-tuning, RAG, embeddings, vector databases, and agent frameworks. Solid data engineering instincts, comfortable wrangling messy data, designing feature pipelines, and synthesizing insights from large or unstructured datasets. Excellent communication skills, with the ability to whiteboard ideas, debate trade-offs, and converge on solutions in real time during collaborative discussions. A genuinely innovative mindset, you experiment, prototype quickly, and aren't afraid to challenge assumptions or propose unconventional approaches. High adaptability, comfortable navigating ambiguity, shifting priorities, and evolving technical landscapes. Self-directed and effective in a remote-first environment, with strong written communication and the discipline to drive work forward asynchronously.

Nice to Have

We don't expect deep hands-on experience across every tool listed below. What matters is that you've shipped production work on at least one tech stack in each area and have enough working understanding of the alternatives to be effective on day one and ramp up quickly where needed.

Startup and product experience: Shipping AI products in a startup or zero-to-one environment, wearing multiple hats across research, engineering, and product. Comfort working directly with founders or technical leadership, translating fuzzy ideas into shipped prototypes within days, not quarters.

AI infrastructure: Hands-on experience with at least one on-premise or air-gapped AI infrastructure stack (NVIDIA DGX, Kubeflow, Ray, or vLLM), with conceptual familiarity with the others.

LLM fine-tuning and serving: Practical experience fine-tuning at least one family of open-source LLMs (Llama, Mistral, Qwen, or DeepSeek), with working knowledge of efficient serving techniques such as quantization, batching, or distillation.

Evaluation and observability: Hands-on use of at least one evaluation or observability framework for LLM applications (Ragas, DeepEval, LangSmith, or similar), with awareness of the broader tooling landscape.

Bonus signals: Contributions to open-source AI projects, technical writing, or a track record of public building. Background in responsible AI practices and data privacy in regulated environments.

Qualifications

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience. 3+ years of applied ML/AI engineering experience, with at least one project involving generative models or AI agents in production.

Why Join Us

Fully remote, async-friendly, and outcome-driven. You'll work on problems that don't have textbook answers, shape AI strategy from the ground up, and have the autonomy to bring your ideas to life, all while building systems that run securely in our own environment.