Machine Learning Engineer

Bespoke Technologies, Inc.; McLean, Northern (VA, KY); Híbrido; USD 120.000 - 170.000; Jornada completa

Descripción de la vacante

Bespoke Technologies, Inc. in McLean is seeking an experienced Machine Learning Engineer to develop and adapt LLM/NLP solutions using AI/ML frameworks to improve analytic workflows and surface relevant information from massive data stores.

You will collaborate with data scientists, preprocess data, design advanced ETL, write SQL, and produce ad-hoc reports and data visualizations, documenting methods on Github and assisting staff with technical training; Poly clearance is required.

Formación

• Experience tuning neural networks and LLMs on custom data.
• Experience developing models and ensembles; selecting Python libraries and preprocessing actions.
• Proficiency in Python.
• Experience with deep learning frameworks such as PyTorch, TensorFlow, or Keras.
• Strong SQL skills including CTEs, set operations, and nested queries.
• Experience with version control systems such as Git and Jenkins.
• Experience leveraging GPUs for accelerated computing.

Responsabilidades

• Collaborate with data scientists to implement requirements and priorities.
• Fine tune, transfer learning, and ensemble models for various domains.
• Analyze large amounts of raw data, including text data, for model utility.
• Preprocess/clean structured and unstructured Sponsor data.
• Design and implement advanced ETL code and table configurations.
• Use SQL to develop and organize relevant information.
• Author analytic publications and ad-hoc reports with data visualizations; document methods on Github.
• Implement Sponsor coordination processes.
• Provide technical education to staff on an ad-hoc basis.
• Serve as subject matter expert in AI/ML to support Sponsor initiatives.

Conocimientos

LLM tuning

Python

Model ensembles

SQL

Data analysis

GPU acceleration

Herramientas

PyTorch

TensorFlow

Keras

Git

Jenkins

HuggingFace

Tableau

Flask

Descripción del empleo

BT-437 – Machine Learning Engineer

Location: McLean

**Please do NOT apply if you do not have an active Poly clearance. Those without a Poly will not be considered.**

Introduction

The Sponsor seeks to develop and adapt Large Language Models (LLM) and Natural Language Processing (NLP) methodologies using artificial intelligence and machine learning (AI/ML) frameworks and programming languages to create new capabilities to improve analytic workflows and address key intelligence questions. The Sponsor needs experienced AI/ML engineering skills to build tools and analytics that use the Sponsor’s structured and unstructured data to yield novel analytic insights, accelerate workflows, enable analysts to surface relevant information from massive data stores. The work will be performed within a team environment.

Work Requirements

Artificial Intelligence and Machine Learning (AI/ML) Engineering – HRR: NO

• The Candidate shall work closely with the Sponsor’s data scientists and technical team to implement requirements; however, the Sponsor’s GTM will manage the priorities.
• The Candidate shall leverage open source best-in-class models to create new models—through fine tuning, transfer learning, and more—and ensembles that can be re-purposed and reused, covering the following domains:
  • Classification
  • Supervised (CNN, Unet, YOLO, or similar)
  • Unsupervised (DBSCAN, K-Nearest, Adaboost, or similar)
  • Prediction
  • Predictors (LSTM, UConv, or similar)
  • Regressors (Bayesian, linear, or similar)
  • Fusion (sensor level and entity level)
  • Decision making (Task assignments, combinational and parametric optimizations, Reinforcement Learning, Path Planning, or similar)
  • Generative (LLMs, Stackable LLMs, or similar)
  • Machine Learning, AI, Deep Learning (TensorFlow, PyTorch), Text, (Classification, NLP, Topic and Language Modeling, Sentiment Analysis, Information Retrieval), Recommender Systems and Personalization, Threat Detection, Computer Vision, Data Mining, Statistics, or similar.
• The Candidate shall analyze large amounts of raw data, including text data, to determine utility for training or testing AI models.
• The Candidate shall preprocess or clean structured and unstructured Sponsor data, including text data.
• The Candidate shall design and implement advanced ETL code and table configurations for complex data sets.
• The Candidate shall use Structured Query Language (SQL) to develop and organize relevant information.
• The Candidate shall in cooperation with a team, author analytic publications and produce ad-hoc reports to include data visualizations using the Sponsor’s templates, and document methods in Github.
• The Candidate shall implement the Sponsor’s existing coordination process.
• The Candidate shall provide technical education to staff on an ad-hoc basis.
• The Candidate shall provide subject matter expertise in AI/ML to support Sponsor’s initiatives.

Required Skills and Demonstrated Experience

• Demonstrated experience tuning neural networks, such as LLMs, on custom data sets and applying results to specific use cases.
• Demonstrated professional or academic experience developing models and ensembles in the AI/ML space, including selecting the best Python libraries for a given task, choosing appropriate pre-processing actions, performing analysis, and assessing model performance.
• Demonstrated professional or academic experience using Python or R.
• Demonstrated professional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras.
• Demonstrated professional or academic experience and proficiency with SQL to include using common table expressions, set operations, aggregated functions and nested subqueries.
• Demonstrated professional or academic experience with version control systems such as Github and Jenkins.
• Demonstrated experience leveraging GPUs for accelerated computing.

Highly Desired Skills and Demonstrated Experience

• Demonstrated professional or academic experience with the HuggingFace Transformers library and hub.
• Demonstrated experience with cloud computing development and architecture.
• Demonstrated experience with front-end web development frameworks such as Flask.
• Demonstrated experience creating machine learning models that conduct text classification and topic modeling in Python using standard machine learning (SciKit Learn-) or deep learning models.
• Demonstrated experience developing applications for semantic search.
• Demonstrated professional or academic experience and proficiency with Tableau to produce visualizations and dashboards.
• Demonstrated academic or professional experience communicating methodological choices and model results.
• Demonstrated experience with verification and validation test benches.
• Demonstrated experience with Explainable AI (XAI) techniques.
• Demonstrated experience with ONNX (Open Neural Net Exchange).