Senior Machine Learning Engineer

Doist; Arlington, Northern (VA, KY); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Health insurance

Dental coverage

Vision coverage

Flexible Spending Account

Performance bonuses

Profit-sharing

Unlimited leave

401k matching

Training budget

Job summary

BizFirst is assisting our client in Arlington, Virginia, with the search for a Senior Machine Learning Engineer to design, build, and deploy production-grade ML systems. This high-impact role centers on AI transformation, spanning data pipelines, model development, and production deployment in a fast-moving environment.

Ideal candidates have 7-10 years in machine learning engineering, strong production ML experience, and hands-on work with ML frameworks.

Qualifications

• 7-10 years of experience in machine learning engineering or applied ML (production systems).
• Expert-level proficiency in PyTorch, TensorFlow, or equivalent frameworks.

Responsibilities

• Design, develop, and deploy scalable machine learning models and pipelines into production environments.
• Translate business problems into well-scoped ML solutions in collaboration with data scientists, engineers, and stakeholders.
• Build and maintain end-to-end ML pipelines from data ingestion to model serving and monitoring.
• Lead model evaluation, A/B testing, and performance monitoring across deployed systems.
• Partner with MLOps and platform engineering to ensure reliable model deployment.

Skills

ML engineering

Python

Distributed systems

Docker

Kubernetes

PyTorch

TensorFlow

Cloud platforms

Data processing

Collaboration

Education

MS or PhD in ML/CS

Tools

MLflow

Weights & Biases

Kubeflow

Spark

Ray

Job description

Senior Machine Learning Engineer

Location: Hybrid - Arlington, Virginia

Employment Type: Full-time

BizFirst is assisting our client with the hiring of a Senior Machine Learning Engineer to help design, build, and deploy production-grade machine learning systems that will fundamentally reshape how the organization operates internally. This is a high-impact role at the center of the client's AI transformation effort, working across data pipelines, model development, and production deployment in a collaborative, fast-moving environment. Our client is a mid-market professional services organization that is actively rethinking how it designs and executes its core business operations through artificial intelligence and automation. The company is building a dedicated AI capability to embed machine learning and generative AI into its most critical internal workflows - from decision support and process automation to real-time analytics and intelligent document processing.

What will you do

The ideal candidate will have significant experience (7-10 years) in machine learning engineering, with a strong background in building and shipping models at scale in production environments. Experience working on large-scale data systems and collaborating closely with data scientists, product teams, and platform engineers is essential. Hands-on experience with large language models (LLMs) and generative AI frameworks is strongly preferred.

Responsibilities:

• Design, develop, and deploy scalable machine learning models and pipelines into production environments.
• Translate business problems into well-scoped ML solutions in close collaboration with data scientists, engineers, and business stakeholders.
• Build and maintain end-to-end ML pipelines from data ingestion and feature engineering through model serving and monitoring.
• Lead model evaluation, A/B testing, and ongoing performance monitoring across deployed systems.
• Partner with MLOps and platform engineering teams to ensure reliable, reproducible, and cost-effective model deployment.
• Drive technical decisions on ML frameworks, model architectures, and tooling standards across the AI practice.
• Mentor and develop junior ML engineers, establishing team-wide engineering standards and code quality practices.
• Document model design decisions, experiment results, and deployment configurations to support organizational learning.

Requirements:

US Citizen or Permanent Resident authorized to work in the United States.

Experience: 7-10 years of experience in machine learning engineering or applied ML, with a strong emphasis on production systems.

ML Frameworks: Expert-level proficiency in PyTorch, TensorFlow, or equivalent frameworks, with a proven record of shipping models to production.

Engineering: Advanced Python skills; comfort with distributed systems, containerization (Docker/Kubernetes), and cloud-based ML infrastructure (AWS, GCP, or Azure).

Data: Solid command of feature engineering, data versioning, and large-scale data processing (Spark, Ray, or similar).

Collaboration: Strong ability to work across technical and non-technical stakeholders, clearly communicating model behavior, tradeoffs, and limitations.

Preferred: Hands-on experience with large language models (LLMs), fine-tuning, retrieval-augmented generation (RAG), or prompt engineering pipelines.

Familiarity with MLOps platforms such as MLflow, Weights & Biases, or Kubeflow.

Experience building AI-powered internal tools, copilots, or automation workflows.

Background in enterprise or professional services environments.

Advanced degree (MS or PhD) in Machine Learning, Computer Science, Statistics, or a related field.

Benefits:

• Family Health Care (54% cost covered for the entire family)
• Family Dental (54% cost covered for the entire family)
• Family Vision (54% cost covered for the entire family)
• Flexible Spending Account
• Performance bonuses tied to project and delivery milestones
• Lifetime Event Bonuses (e.g., new child, marriage)
• Profit-sharing arrangement for any work brought into the company
• Unlimited Leave with Approval
• 401k - 100% employer match on first 4% invested
• $1,500 annual training and conference budget

Job Type: Full-time, Permanent Position

Work Authorization: US Citizen or Permanent Resident; no active security clearance required.

Schedule: Monday to Friday

Work Location: Hybrid - Arlington, Virginia