ML Engineer

coherentsolutions; United States; Hybrid; USD 90,000 - 130,000; Full time

Benefits offered by this job

Health insurance

Remote and hybrid work options

Mentorship pairing

Internal conferences and training

Job summary

Coherent Solutions is seeking an early-career machine learning engineer to join a team of senior practitioners. You will work on internal ML initiatives, moving models from notebooks into production services and building reproducible pipelines.

Responsibilities include data cleaning, exploratory analyses, feature engineering, model benchmarking, and deploying models via REST APIs or batch workflows, with emphasis on testing and documentation.

Qualifications

• Hands-on Python development with pandas, NumPy, scikit-learn, and Jupyter.
• Solid foundation in statistics, probability, and linear algebra; comfortable with EDA.
• Experience with supervised and unsupervised learning and core task families.
• Ability to prepare structured datasets with feature engineering and selection.

Responsibilities

• Clean, transform, and validate structured datasets with quality checks.
• Run exploratory analyses to surface patterns and modeling approaches.
• Build, test, and benchmark models across regression, classification, clustering, and more.
• Engineer features and tune hyperparameters with validation strategies.
• Maintain reproducible pipelines, notebooks, and documentation.
• Add automated tests for data and modeling code; ship models via REST APIs or batch workflows.
• Communicate model assumptions, limitations, and trade-offs clearly.

Skills

Python

Pandas

NumPy

Scikit-learn

Jupyter

Statistics

EDA

Feature engineering

SQL

Git

Docker

Tools

Jupyter

Docker

Git

Job description

Role overview

This position is an early-career machine learning engineering role embedded with senior practitioners on internal classical ML initiatives. The work blends structured learning, hands-on delivery, and progressively broader ownership, with the goal of building reliable, reproducible models that move from notebooks into production services.

Responsibilities

• Clean, transform, and validate structured datasets, handling missing values, encoding, scaling, and quality checks before modeling
• Run exploratory analyses to surface patterns, anomalies, and candidate modeling approaches
• Build, test, and benchmark models across regression, classification, clustering, dimensionality reduction, and anomaly detection
• Engineer and select features in collaboration with senior engineers, and tune hyperparameters against chosen validation strategies
• Maintain reproducible pipelines, experiment tracking, notebooks, and technical documentation for data and models
• Add automated tests for data and modeling code, and help ship models through REST APIs or batch workflows
• Communicate assumptions, limitations, and trade-offs of model behavior to peers and stakeholders

Requirements

• At least one year of hands-on Python development with pandas, NumPy, scikit-learn, and Jupyter
• Solid foundation in statistics, probability, and linear algebra, plus comfort with exploratory data analysis
• Working knowledge of supervised and unsupervised learning and practical experience with the core task families listed above
• Skill in preparing structured datasets, including feature engineering and feature selection
• Understanding of train/validation/test splits, cross-validation, leakage, overfitting, regularization, and metric selection
• SQL ability for data extraction and analysis, plus familiarity with Git, automated testing, Docker, and basic model monitoring
• English at B2 level or higher

Nice to have

• Experience with XGBoost or LightGBM, time-series work, recommendation systems, MLflow, and model interpretability techniques
• Cloud services, production ML monitoring, and exposure to GenAI, large language models, retrieval-augmented generation, prompt engineering, or agent development

Benefits and work setup

• Technical and non-technical training programs for professional and personal growth
• Internal conferences and meetups featuring outside experts
• Mentorship pairing with an experienced engineer
• Health insurance coverage
• Sports and wellness activities
• Remote and hybrid work options
• Employee referral program
• Work-anniversary recognition and additional vacation days