MACHINE LEARNING ENGINEER JOBS

Machine Learning Engineer Jobs

Build intelligent systems. ML and AI at innovative startups.

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What is a Machine Learning Engineer?

ML engineers build machine learning systems for production. At startups, you'll take models from research to deployment, building the infrastructure and pipelines that make ML products work reliably at scale.

Salary Ranges

$90k - $130k
Junior (0-2 years)
$130k - $190k
Mid-Level (2-5 years)
$190k - $300k
Senior (5+ years)

Typical Responsibilities

Build and train ML models

Deploy models to production

Create ML pipelines and infrastructure

Monitor model performance

Optimize for latency and cost

Collaborate with data scientists and engineers

Required Skills

Python
ML frameworks (PyTorch, TensorFlow)
Data processing (pandas, NumPy)
SQL
Basic software engineering
Statistics and math

Nice to Have

MLOps tools
Cloud ML services
Deep learning
NLP or Computer Vision
Distributed computing
Feature stores

Career Path

Entry
Junior ML Engineer
0-2 years
Mid
ML Engineer
2-5 years
Senior
Senior ML Engineer
5-8 years
Staff
Staff ML Engineer
8+ years
Lead
ML Tech Lead / Manager
6+ years

Interview Tips

Know ML Fundamentals

Understand bias/variance, overfitting, common algorithms. Be ready to explain concepts clearly.

Show Production Experience

ML engineering is about production. Discuss how you've deployed models, monitored them, and handled failures.

Code Competence

ML engineers are software engineers too. Be prepared for coding interviews alongside ML questions.

Discuss Trade-offs

Accuracy vs latency, simple vs complex models—show you understand practical ML decision-making.

Profile Tips for Machine Learning Engineers

Show deployed ML projectsInclude Kaggle or competition resultsMention specific frameworks and toolsExplain business impact of modelsDescribe scale and performance achieved
FAQ

Frequently Asked Questions

Data scientists focus on analysis and model development; ML engineers focus on production deployment and infrastructure. There's overlap, but ML engineering is more software engineering focused.

Not for most startup roles. A strong portfolio, understanding of ML fundamentals, and software engineering skills matter more. PhDs are common but not required.

Very high demand, especially for engineers who can deploy models to production. Pure research is competitive; production ML engineering has strong job market.

Combine courses (fast.ai, Coursera) with projects that deploy models. Kaggle for modeling skills, personal projects for production experience. Building end-to-end systems is key.

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