AI/ML Engineer

Tennesee Urology Associates, PLLC
United States
1 day ago
Apply on job-boards.eu.greenhouse.io
Prepare application

Role details

Contract type
Internship / Graduate position
Employment type
Full-time (> 32 hours)
Experience level
Starter
Compensation
$30,000.0
Working hours
Regular working hours
Languages
English

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Computer Vision Information Engineering Elasticsearch Python (Programming Language) Machine Learning Object-Oriented Software Development Software Engineering Apache Solr Pytorch
+14 more
Large Language Models Concurrency Parallel Computation Git Fastapi Pandas Kubernetes Information Technology HuggingFace Xgboost Codebase Machine Learning Operations GPT Docker

Job description

Work as a hands-on junior ML engineer building generative AI apps, traditional ML models, and MLOps solutions. Implement and maintain model training, inference pipelines, and LLM systems while being mentored by senior engineers and delivering client projects. The summary above was generated by AI

Build the Next Generation of AI Products with TensorOps

TensorOps is an applied machine learning and artificial intelligence studio helping organizations worldwide plan, design, train, and deploy production-grade ML systems. Our clients range from NASDAQ-listed enterprises to seed-stage startups. Projects span from small proofs-of-concept to multi-year strategic initiatives.

What We’re Working On:

  • Generative AI applications: Chatbots and Agents
  • Traditional Machine Learning: Time Series Forecasting, AdTech, Computer Vision, etc.
  • MLOps: Improving ML pipelines at scale

Core Stack: As we work with many clients, our stack varies, but we often use:

  • Python APIs: FastAPI
  • Containerization: Docker, Kubernetes
  • Model Training & Serving: LightGBM, CatBoost, PyTorch, HuggingFace
  • Data Engineering: Pandas, Polars
  • LLM Frameworks: LangChain, LangGraph
  • Observability: MLFlow, Langfuse
  • Cloud Platforms: AWS, GCP
  • Search: Elasticsearch, OpenSearch, Solr, We’re looking for a Junior Machine Learning Engineer to help us deliver projects rapidly. You’ll report to and be mentored by a senior team member. This is a hands-on role from day one, working on real projects that make a tangible impact.

Requirements

  • BSc in Computer Science, Software Engineering or equivalent
  • MSc in Computer Science, Data Science, AI or equivalent, * Solid software engineering fundamentals (OOP, Git, concurrency, parallelism)
  • Proficiency in Python
  • Understanding of LLM system design (RAG, agents, etc.)
  • Knowledge of ML system design (pipelines, training/inference techniques)
  • Excellent English communication skills

Nice to Have:

  • Experience in non-academic projects (jobs, internships or similar)
  • Previous LLM projects (academic or otherwise)
  • Exposure to AI features in cloud platforms (Sagemaker, Bedrock, Vertex AI)
  • Experience working in large codebases

Benefits & conditions

  • Fully remote (legal residence in Portugal required)
  • Real-world projects, rapid feedback loops, and measurable impact
  • Mentorship from engineers who have shipped ML systems at scale
  • Competitive compensation and growth opportunities - your growth will be based on ownership and performance rather than periodic reviews (which we still do)

Compensation & Perks:

  • Yearly salary: €30,000
  • Travel expenses allowance
  • Urban Sports Club membership
  • Free Professional Certifications

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on job-boards.eu.greenhouse.io
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:37 min

Optimizing technical profiles for AI sourcing and recruitment

Mina Golesorkhi Mina Golesorkhi · World Congress 2026 Europe

40 sec

Generative pre-trained transformer models powering code completions

lgonta lgonta +1 · World Congress 2024

6:21 min

Investigating push inefficiencies with upstream Git experts

Jonathan Creamer · Coffee With Developers

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · World Congress 2025

4:35 min

Learning resources and community engagement for AI engineers

Alfonso Graziano Alfonso Graziano · Coffee With Developers

51 sec

Assessing GPT-4o performance for pull request feedback

Merrill Lutsky Merrill Lutsky · World Congress 2025

Videos

See all

Related articles

See all