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About This Role
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.
Type: Remote, Full-time
Role Overview
We’re hiring a Senior Machine Learning Engineer to architect, build, and deploy high-performance machine learning systems that power technology stack. You will work across the entire ML lifecycle—from processing massive volumes of data to developing and deploying low-latency models.
You must possess a strong hybrid skill set: deep expertise in applied machine learning combined with production-grade software engineering skills. You will not just build models in notebooks; you will write scalable, production-ready code, design real-time inference APIs, and ensure your systems meet strict latency and high-throughput requirements.
Key Responsibilities
Scale Data Engineering & Feature Pipelines
- Process and extract features from massive, highly sparse datasets (terabytes/petabytes of bidstream and user event data) using SQL, Python, and distributed computing frameworks (e.g., Spark, Ray).
- Architect offline and online feature pipelines. Manage real-time feature computation and low-latency feature stores ensuring zero online/offline skew.
- Perform rigorous missingness analysis, leakage checks, and handle high-cardinality categorical variables safely.
Core ML & Deep Learning Development
- Train, tune, and scale supervised learning models, utilizing advanced gradient boosting (XGBoost, LightGBM, CatBoost) and Factorization Machines.
- Design and implement Deep Learning architectures for structured/recommendation data using PyTorch or TensorFlow.
- Apply rigorous tabular modeling practices: meticulous leakage prevention, class imbalance strategies, and robust cross-validation on time-split data.
Productionization, MLOps, & System Engineering
- Write clean, object-oriented, and modular production code. Transition models from Python research environments to high-performance serving environments (packaging with ONNX, TensorRT, etc).
- Design and maintain robust MLOps pipelines: automated model retraining, versioning, shadow deployments, and CI/CD for machine learning.
- Monitor production models for data drift, concept drift, and performance degradation in real-time, implementing automated alerting and fallback mechanisms.
Evaluation & Experimentation
- Design rigorous A/B and multivariate tests to measure the true business incrementality of ML models.
- Choose appropriate offline metrics (PR-AUC, normalized Entropy/LogLoss, Calibration, Lift) and bridge them to online business KPIs.
Success in This Role Looks Like
- You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency).
- Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring.
- Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly.
Required Qualifications
- 5–8+ years of experience as a Machine Learning Engineer or Software Engineer focusing on ML systems, ideally within Ad Tech, MarTech, or high-scale recommendation systems.
- Production Engineering Skills: Strong software engineering fundamentals (OOP, data structures, algorithm design). Expert-level Python and strong proficiency in a compiled or high-performance language (e.g., C++, Java, Scala, Go, or Rust).
- ML Systems & Serving: Deep experience deploying machine learning models into highly concurrent, low-latency production environments (APIs, microservices, Triton Inference Server, custom containers).
- Distributed Computing: Hands-on experience with big data processing (Apache Spark, Kafka, Flink) and complex SQL queries.
- Core ML & Deep Learning: Proven track record of shipping both tree-based models and neural networks (PyTorch/TensorFlow) to production.
- Statistics & Experimentation: Solid grasp of statistics, hypothesis testing, and rigorous A/B experiment design.
Nice-to-Have
- Agentic / GenAI Development: Experience designing agentic workflows or utilizing LLMs to automate ad creative generation, campaign copilot tools, or internal ML development workflows (AI-assisted IDEs, code agents).
Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
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Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fusemachines, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Fusemachines AI Hiring
Fusemachines has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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