Founding Senior AI​/ML Engineer

We are an early‑stage startup in Boston, MA, and Buffalo, NY, building cutting‑edge AI‑driven solutions (with applications in computer vision, LLM/VLM/VLA, and edge computing). At the MVP stage, we’re focused on rapid R&D and in‑person collaboration tackling cutting‑edge AI and engineering challenges to bring edge and aero intelligence to life. We have secured initial funding and resources, and will be based in a local collaborative incubator space.

Buffalo, NY (on‑site preferred; hybrid/remote considered for strong candidates)

Work directly with the founders and lead the development of our AI/ML systems from the ground up to production, with hardware integration. This is a unique opportunity to join us in a fast‑paced, ownership‑driven environment where your work will directly shape our product and technical direction. If you are an experienced hands‑on engineer or builder who can wear multiple hats, from developing state‑of‑the‑art AI/ML models to deploying code in production, and you’re excited to grow with a company from the ground up, thrive in a fast‑paced startup environment, we’d love to speak with you!

• Legal Authorization to Work:
  Must be legally authorized to work in the U.S. without any restrictions at the time of hire. We are unable to offer visa sponsorship (e.g., H-1B, F-1 OPT extension, etc.) for this position.
• Education & Experience:
  
  Bachelor’s or Master’s, Doctorate degree (MS and Ph.D. preferred) in AI, Computer Science/Engineering, Data Science/Engineering, or a related field (or equivalent hands‑on experience). 5+ years of industry hands‑on experience in machine learning, data science, or software engineering roles that involved developing and deploying AI/ML models. Proven experience taking ML projects from initial idea to deployment is a must. Startup experience is a big plus (we need someone who can operate with limited resources and ambiguous requirements).
• Machine Learning & Deep Learning Expertise:
  Strong knowledge of ML algorithms and deep learning techniques. Experience training and fine‑tuning models in areas like LLM/VLM/VLA, computer vision, reinforcement learning, or multimodal models with a deep understanding of the relevant knowledge. You should be comfortable reading research papers or OSS model release notes and implementing improvements. Experience with cutting‑edge AI models (e.g., vision‑language models, large language models, or segmentation/detection models) is ideal.
  
  Experience in distributed training and fine‑tuning on cloud and HPC setups is preferred.
• Programming & Frameworks:
  Proficiency in Python, C/C++, and common ML frameworks/libraries (such as PyTorch, Tensor Flow, Numpy, Pandas, OpenCV, etc.). Solid software engineering practices, writing clean, efficient code, using version control (Git), and structuring projects for collaboration are required. Data engineering skills (SQL databases, data pipelines) are essential, as is understanding how to design systems that are efficient and maintainable.
• Full‑Stack Engineering Ability:
  While ML is the focus, this role is “full‑stack” in the sense that you should be able to handle connecting the ML components with a product. Backend development skills are essential, e.g., experience building APIs or microservices (Python backends like Flask/FastAPI or Node.js) to serve model results. Comfort with databases and basic queries (for storing results or training data) is helpful.
  
  Some front‑end or embedded/edge experience is a plus (e.g., if we develop a simple web demo, or deploy to a mobile app, you can assist in integration). Overall, you should be able to develop end‑to‑end solutions around the ML model, not just the model in isolation.
• Cloud & MLOps/Dev Ops:
  Experience deploying applications or services to production on cloud platforms (AWS, GCP, or Azure) and edge devices. For example, you know how to spin up EC2 or GCE instances, use S3 or Cloud Storage, and possibly utilize container services (Docker, Kubernetes). You understand concepts such as containerization, serverless functions, and CI/CD pipelines for automated deployments. Specific experience with GPU cloud…