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Build the Next Generation of AI Products with TensorOps
TensorOps is an applied-machine-learning studio that helps organisations across Europe and North America design, train, and deploy production-grade GenAI systems. Our team blends research depth with pragmatic engineering, and we’re looking for experienced engineers to help us build and scale our solutions.
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
The Role
As a Mid-Level Machine Learning Engineer, you will be a key contributor to our project teams, taking ownership of core components and shipping robust AI/ML systems. This is a hands-on role from day one, working on real projects that make a tangible impact.
You will:
Design, build, and maintain production-grade ML systems, from data ingestion and processing to model deployment and monitoring.
Develop and fine-tune generative AI models, including LLMs, for specialized tasks. You'll move beyond prototyping to build robust, scalable solutions.
Architect and implement reliable data pipelines and low-latency inference services using our core stack (FastAPI, Docker, Kubeflow, AWS/GCP).
Collaborate with senior engineers, researchers, and client stakeholders to translate business problems into technical solutions and deliver tangible value.
Take ownership of key components of our ML platform, ensuring code quality, performance, and scalability.
About You
3+ years of professional experience in a software engineering or machine learning role.
Strong proficiency in Python and its data science ecosystem (e.g., Pandas, NumPy, Scikit-learn).
Hands-on experience building and shipping models using at least one major ML framework.
Proven experience with the practical application of Large Language Models (LLMs). Familiarity with frameworks like LangChain/LangGraph and retrieval-augmented generation (RAG) is a significant plus.
Solid understanding of software engineering best practices, including version control (Git), testing, CI/CD, and containerization (Docker).
A BSc/MS in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
Why TensorOps?
- 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: €48,000-60,000
- Travel expenses allowance
- Urban Sports Club membership
- Free Professional Certifications
TensorOps New York, New York, USA Office
New York, New York, United States
What you need to know about the NYC Tech Scene
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
