ML Engineer

Coherent Solutions
Moldova
Remote

Who this role is best for

Best suited to early-to-mid-career data professionals with a focus on classical machine learning, working in a collaborative engineering environment in Moldova.

Best fit for

  • Early-to-mid-career data professionals with classical ML expertise and a collaborative mindset
    — “work with senior engineers on internal classical machine learning initiatives”
  • Candidates who value structured learning and technical documentation as part of their growth
    — “help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation”

Things to consider

  • English proficiency at B2 level or higher is a hard requirement for communication and documentation
    — “English at B2 level or higher”
  • The role emphasizes working with senior engineers and taking on increasing responsibility
    — “gradually take ownership of more complex tasks”

How to stand out

  • Showcase your ability to deliver models via REST APIs or batch workflows in your portfolio or interviews
    — “support the delivery of models through REST APIs or batch-processing workflows”
  • Demonstrate strong feature engineering and dataset preparation skills with concrete examples
    — “experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling”
  • Emphasize your familiarity with model evaluation and error analysis techniques
    — “select appropriate evaluation metrics, compare models, and perform error analysis”
  • Include examples of cross-validation, data leakage prevention, and regularization in your ML projects
    — “understanding of train/validation/test splits, cross-validation, data leakage, overfitting, and regularization”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Prepare and validate structured datasets for machine learning tasks
  • Build, test, and compare ML models for various tasks
  • Help maintain reproducible ML pipelines and technical documentation
  • Support the delivery of models through REST APIs or batch-processing workflows
Typical background
1+ year of hands-on Python development experienceStrong foundation in statistics, probability, and linear algebraExperience with supervised and unsupervised learning

Skills & requirements

Required

PythonPandasNumPyscikit-learnJupyterSQLXgboostLightgbmMlflowFastapiGitDockerPytest

Preferred

Time-series AnalysisRecommendation SystemsCloud ServicesProduction ML MonitoringGenaiLLMRAGPrompt-engineeringAgent-development

Stack & domain

PythonPandasNumPyscikit-learnJupyterSQLXgboostLightgbmMlflowFastapiGitDockerPytestLeadershipCommunicationData ScienceMachine Learning

About the role

Original posting from Coherent Solutions via Greenhouse

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, Jupyter

SQL

XGBoost, LightGBM

MLflow

FastAPI

Git, 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

The global benefits package includes:

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.

Source: Coherent Solutions careers (Greenhouse)

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