Machine Learning Engineer

About Fusemachines

Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail,  manufacturing, and government.

Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.

Role Overview

We're hiring Mid and Senior Data Scientists / ML Engineers to help us build the next generation of intelligent, agentic systems that solve real business problems. At this level, you'll own problems end-to-end: scoping ambiguous business questions with AI Leads, choosing the right modeling approach (classical ML, foundation models, or agentic architectures), shipping to production, and iterating based on real-world signals. Senior candidates will additionally shape technical direction, mentor others, and raise the bar for how the team uses modern AI tooling.

Whether your depth is in NLP, Computer Vision, Multimodal systems, or classical ML, what we care about is your ability to reason from first principles, leverage modern AI tools to multiply your output, and turn ambiguous problems into shipped systems that move the business.

Core Responsibilities

• Own ML projects end-to-end - from problem framing and data exploration through modeling, deployment, monitoring, and iteration. 
• Design and ship production ML systems spanning churn and propensity modeling, demand forecasting, anomaly detection, pricing optimization, and recommendation - using the right tool for the job, whether that's a gradient-boosted model, a fine-tuned foundation model, or an agent-based workflow.
• Build agentic AI workflows that automate decision-making across the business - including multi-agent systems, tool-using agents, and human-in-the-loop pipelines that combine LLM reasoning with deterministic logic and traditional ML.
• Leverage modern AI development tooling (AI coding assistants, agentic IDEs, automated evaluation harnesses, MLOps platforms) as a core part of your engineering practice - not an afterthought.
• Apply foundation models and domain-specific ML to extract signals from structured data, text, images, audio, or multimodal inputs depending on the problem at hand.
• Design rigorous evaluations for both deterministic models and probabilistic AI systems - offline metrics, online A/B tests, LLM-as-judge frameworks, and regression suites for non-deterministic outputs.
• Partner with data and platform engineers to build robust pipelines, feature stores, and serving infrastructure supporting both classical ML and modern AI workloads.
• Communicate effectively with technical and non-technical stakeholders - translating model behavior, tradeoffs, and risks into decisions the business can act on.
• (Senior) Set technical direction for an area of the ML stack, mentor mid-level and junior engineers, lead design reviews, and influence the team's tooling, evaluation standards, and architectural choices.

Technical Requirements

• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related quantitative field - or equivalent practical experience.
• 3+ years of applied data science / ML experience with a track record of shipping models to production. (Senior: 5+ years) including ownership of complex systems and demonstrable technical leadership.
• Strong programming proficiency in Python, including modern ML/AI libraries (PyTorch or JAX, scikit-learn, Hugging Face, pandas etc.).
• Solid grounding in statistical modeling, experimentation, and classical ML - regression, tree-based methods (Random Forest, XGBoost, LightGBM), time series forecasting, and anomaly detection.
• Hands-on experience with at least one specialty area: NLP and LLMs, Computer Vision, or Multimodal systems. We're domain-agnostic - depth in any of these is valued.
• Practical experience working with foundation models and LLM APIs - prompt design, structured outputs, function calling, retrieval-augmented generation (RAG), and fine-tuning where appropriate.
• Comfort with modern AI development workflows - using AI coding assistants and agentic IDEs (Claude Code, Cursor, Copilot, or similar) as a daily part of your engineering practice.
• Experience with data engineering fundamentals and big-data tooling such as PySpark, Databricks, or equivalent distributed compute frameworks.
• Familiarity with MLOps and LLMOps practices - version control for models and prompts, experiment tracking, CI/CD for ML, monitoring, and evaluation pipelines.
• (Senior) Demonstrated ability to make architectural decisions, navigate ambiguity, and influence cross-functional partners on technical strategy.