Box N Case
Machine Learning Engineer — Niche but massively sought-after in 2025.
Box N Case, Commack, New York, United States, 11725
Machine Learning Engineer — Niche but massively sought-after in 2025.
Commack, United States | Posted on 12/16/2025
BoxNCase (www.boxncase.com) is your trusted source for gourmet culinary essentials, serving businesses and individuals across North America. Based in the New York City Area, we take pride in being one of the largest direct importers and wholesalers specializing in the world of Specialty & Fine Foods, offering a diverse array of premium products in club sizes. Our commitment is rooted in bringing you a diverse selection of top‑tier specialty and fine food products, thoughtfully sourced from around the globe. Discover a curated selection of chocolates, candies, snacks, spices, condiments, cheeses, teas, and more, all in bulk sizes. Whether you're stocking up for your business or a home chef with a passion for gourmet creations, our easy ordering, proprietary CPG logistic solutions, and US based account managers cater to your unique needs.
Job Description About the Role
Data is our fuel, but you are the engine. We are looking for a
Machine Learning Engineer
who treats model development as an engineering discipline, not just an experiment.
In this role, you will bridge the gap between data science and production software. You will design
predictive models , build automated
ML pipelines , and ensure our algorithms run efficiently at scale. You are perfect for this role if you love
Python , live in
Linux , and dream in
vectors .
What You Will Do
Model Development:
Design, train, and validate machine learning models (Regression, Classification, Clustering, Deep Learning) to solve business problems.
Production Engineering (MLOps):
Build scalable pipelines using tools like
Kubeflow ,
MLflow , or
Airflow
to automate model training and deployment.
Optimization:
Improve model inference time and reduce latency for real‑time applications.
Data Wrangling:
Write complex SQL queries and build ETL processes to prepare massive datasets for training.
Feature Engineering:
Identify and extract key features from raw data to improve model accuracy.
Collaboration:
Work closely with Data Scientists to take research prototypes and turn them into production‑grade code.
Requirements What We Are Looking For
Experience:
2+ years in Machine Learning Engineering or a heavy Data Science role with production responsibility.
Core Languages:
Advanced proficiency in
Python
(Pandas, NumPy) and familiarity with
C++
or
Java
is a plus.
ML Frameworks:
Deep experience with
TensorFlow ,
PyTorch ,
Scikit‑learn , or
XGBoost .
Math & Stats:
Strong foundation in probability, statistics, and linear algebra.
Cloud Native:
Hands‑on experience with
AWS SageMaker ,
Google Vertex AI , or
Azure ML .
Big Data:
Familiarity with
Spark ,
Hadoop , or
Kafka
for handling large‑scale data streams.
Preferred Tech Stack (Keywords)
Data: SQL, NoSQL, Spark, Databricks
Salary Range: $50,000 – $200,000 USD / year (Based on experience and location)
Flexible Work: 100% Remote or Hybrid options.
Tools: Access to high-performance compute clusters and GPUs.
Growth: Budget for conferences (NeurIPS, ICML) and certifications.
#J-18808-Ljbffr
BoxNCase (www.boxncase.com) is your trusted source for gourmet culinary essentials, serving businesses and individuals across North America. Based in the New York City Area, we take pride in being one of the largest direct importers and wholesalers specializing in the world of Specialty & Fine Foods, offering a diverse array of premium products in club sizes. Our commitment is rooted in bringing you a diverse selection of top‑tier specialty and fine food products, thoughtfully sourced from around the globe. Discover a curated selection of chocolates, candies, snacks, spices, condiments, cheeses, teas, and more, all in bulk sizes. Whether you're stocking up for your business or a home chef with a passion for gourmet creations, our easy ordering, proprietary CPG logistic solutions, and US based account managers cater to your unique needs.
Job Description About the Role
Data is our fuel, but you are the engine. We are looking for a
Machine Learning Engineer
who treats model development as an engineering discipline, not just an experiment.
In this role, you will bridge the gap between data science and production software. You will design
predictive models , build automated
ML pipelines , and ensure our algorithms run efficiently at scale. You are perfect for this role if you love
Python , live in
Linux , and dream in
vectors .
What You Will Do
Model Development:
Design, train, and validate machine learning models (Regression, Classification, Clustering, Deep Learning) to solve business problems.
Production Engineering (MLOps):
Build scalable pipelines using tools like
Kubeflow ,
MLflow , or
Airflow
to automate model training and deployment.
Optimization:
Improve model inference time and reduce latency for real‑time applications.
Data Wrangling:
Write complex SQL queries and build ETL processes to prepare massive datasets for training.
Feature Engineering:
Identify and extract key features from raw data to improve model accuracy.
Collaboration:
Work closely with Data Scientists to take research prototypes and turn them into production‑grade code.
Requirements What We Are Looking For
Experience:
2+ years in Machine Learning Engineering or a heavy Data Science role with production responsibility.
Core Languages:
Advanced proficiency in
Python
(Pandas, NumPy) and familiarity with
C++
or
Java
is a plus.
ML Frameworks:
Deep experience with
TensorFlow ,
PyTorch ,
Scikit‑learn , or
XGBoost .
Math & Stats:
Strong foundation in probability, statistics, and linear algebra.
Cloud Native:
Hands‑on experience with
AWS SageMaker ,
Google Vertex AI , or
Azure ML .
Big Data:
Familiarity with
Spark ,
Hadoop , or
Kafka
for handling large‑scale data streams.
Preferred Tech Stack (Keywords)
Data: SQL, NoSQL, Spark, Databricks
Salary Range: $50,000 – $200,000 USD / year (Based on experience and location)
Flexible Work: 100% Remote or Hybrid options.
Tools: Access to high-performance compute clusters and GPUs.
Growth: Budget for conferences (NeurIPS, ICML) and certifications.
#J-18808-Ljbffr