ML Engineer

Coherent Solutions; New York (NY); Hybrid; USD 90,000 - 130,000; Full time

Benefits offered by this job

Health insurance

Flexible work options

Internal conferences and meetups

Mentorship and growth opportunities

Job summary

Coherent Solutions is seeking a motivated early‑career ML Engineer to join our internal ML initiatives. You'll work with senior engineers on data preparation, model development, and experiments, contributing to production‑level pipelines.

This role emphasizes hands‑on Python, collaboration, and gradual ownership, with flexible remote and hybrid options, health benefits, and opportunities to grow in a global engineering environment.

Qualifications

• 1+ year of hands-on Python development experience with pandas, NumPy, scikit-learn, and Jupyter.
• Strong foundation in statistics, probability, and linear algebra; capable of exploratory data analysis.
• Understanding of supervised and unsupervised learning including regression, classification and clustering.
• Experience preparing structured datasets with cleaning, transformation, encoding, scaling and missing-value handling; feature engineering.
• Knowledge of train/validation/test splits, cross-validation, data leakage, overfitting and regularization.
• Ability to select evaluation metrics, compare models, and perform error analysis; reproducible ML pipelines.
• Familiarity with hyperparameter tuning, Git, Docker, automated testing, and basic model monitoring.
• SQL skills for data extraction and analysis; ability to expose models via REST APIs or batch workflows.
• English at B2 level or higher.

Responsibilities

• Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling
• Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling
• Build, test, and compare ML models for tasks such as regression, classification, clustering, dimensionality reduction, and anomaly detection
• Develop and select features with guidance from senior engineers
• Run experiments, tune model parameters, and evaluate results using appropriate validation approaches and metrics
• Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation
• Write and maintain automated tests for data and model‑related code
• Support the delivery of models through REST APIs or batch‑processing workflows
• Document model assumptions, results, limitations, and technical decisions
• Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks

Skills

Python development

Statistics / math foundation

Exploratory data analysis

ML concepts

Feature engineering

Model evaluation / metrics

Hyperparameter tuning

Data cleaning / preprocessing

English proficiency

Tools

pandas

NumPy

scikit-learn

Jupyter

REST APIs

Docker

Git

SQL

MLflow

XGBoost

Job description

Company Background

Coherent Solutions is a global digital product engineering company founded in 1995 and headquartered in Minneapolis, Minnesota. With a presence in nine countries and a team of more than 1,700 engineers, the company provides custom software development and engineering services across web and mobile applications, cloud, DevOps, and data. Coherent Solutions works with clients worldwide to build and scale digital products while fostering a culture of collaboration, transparency, and continuous professional growth.

Project Description

You will work with senior engineers on internal classical machine learning initiatives. The role combines structured learning, practical delivery, and gradual ownership of increasingly complex tasks.

Technologies

• Python
• pandas
• NumPy
• scikit-learn
• JupyterSQLXGBoost
• LightGBMMLflowFastAPIGit
• Docker
• pytest

What You'll Do

• Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling
• Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling
• Build, test, and compare ML models for tasks such as regression, classification, clustering, dimensionality reduction, and anomaly detection
• Develop and select features with guidance from senior engineers
• Run experiments, tune model parameters, and evaluate results using appropriate validation approaches and metrics
• Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation
• Write and maintain automated tests for data and model‑related code
• Support the delivery of models through REST APIs or batch‑processing workflows
• Document model assumptions, results, limitations, and technical decisions
• Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks

Job Requirements

• 1+ year of hands‑on Python development experience, including experience with pandas, NumPy, scikit‑learn, and Jupyter
• A strong foundation in statistics, probability, and linear algebra, plus exploratory data analysis skills
• An understanding of supervised and unsupervised learning, and practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection
• Experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing‑value handling, plus practical feature engineering and feature‑selection skills
• An understanding of train/validation/test splits, cross‑validation, data leakage, overfitting, and regularization
• the ability to select appropriate evaluation metrics, compare models, and perform error analysis
• Familiarity with hyperparameter tuning and reproducible ML pipelines
• SQL skills for data extraction and analysis
• The ability to expose models through REST APIs or batch‑processing workflows
• Familiarity with Git, automated testing, Docker, and basic model monitoring
• The ability to explain model behavior, assumptions, limitations, and results
• English at B2 level or higher

Nice To Have

• XGBoost, time‑series analysis, recommendation systems, MLflow, model interpretability, cloud services, production ML monitoring, and GenAI, LLM, RAG, prompt‑engineering, or agent‑development experience

What Do We Offer

• Technical and non-technical training for professional and personal growth
• Internal conferences and meetups to learn from industry experts
• Support and mentorship from an experienced employee to help you professional grow and development
• Health insurance
• Sports activities to promote a healthy lifestyle
• Flexible work options, including remote and hybrid opportunities
• Referral program for bringing in new talent
• Work anniversary program and additional vacation days