What is a Machine Learning Engineer?
A machine learning engineer builds the systems that train, deploy, and serve ML models in production. They sit at the intersection of software engineering and data science — writing production-grade Python, designing training pipelines, and ensuring models work reliably at scale. At AI-native startups, MLEs are often among the highest-leverage hires: they turn research into product.
At what stage should you hire a Machine Learning Engineer?
AI-native companies hire ML engineers from day one. For companies adding an AI layer to an existing product, the right time is typically when you've validated that ML will meaningfully improve your core metric and have enough data to train against. Hiring too early (before data exists) wastes the role. Hiring too late means engineers are prototyping in notebooks while your competitors ship.
Common titles for this role
- Machine Learning Engineer
- ML Engineer
- Applied Machine Learning Engineer
- AI/ML Engineer
- Research Engineer
- MLOps Engineer
Typical background
Strong ML engineers combine software engineering fundamentals with applied ML experience. RFS has placed ML engineers from Palantir (where we have 10+ placements), Scale AI, Windsurf, and Mercor. We look for engineers who have shipped ML models to production — not just trained models in notebooks — and understand the full lifecycle from data collection through inference optimization.
What does a Machine Learning Engineer do at a startup?
- Design and implement training pipelines using frameworks like PyTorch, TensorFlow, or JAX
- Build feature engineering, data preprocessing, and dataset curation infrastructure
- Deploy models to production via REST APIs, batch inference, or streaming systems
- Monitor model performance, detect drift, and retrain on updated data
- Optimize model inference for latency and cost (quantization, distillation, caching)
- Collaborate with data scientists on experiment design and with backend engineers on serving infrastructure
- Evaluate and integrate third-party models and APIs (OpenAI, Anthropic, open-source LLMs)
Key skills and qualifications
- Strong software engineering fundamentals: Python, version control, testing, code review
- Hands-on experience with PyTorch, TensorFlow, or equivalent ML frameworks
- Experience building and maintaining ML training and inference pipelines
- Familiarity with cloud ML platforms: AWS SageMaker, GCP Vertex AI, or Azure ML
- Understanding of model evaluation, A/B testing, and production monitoring
- Knowledge of LLM fine-tuning, RAG architectures, or prompt engineering (for AI-era roles)
Why hire your Machine Learning Engineer through RFS?
- 10+ ML engineer placements at Palantir alone — we have deep relationships with ML talent from the best AI companies
- We understand the difference between a research engineer and a production ML engineer — and we screen for what your role needs
- 29-day average time to hire — ML engineer searches are among the most competitive; our network cuts the timeline
- Pre-vetted for production experience: every candidate we present has shipped ML systems, not just trained models
- 90+ NPS — AI-first companies trust us with their most technical searches
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