Senior Machine Learning Engineer

CyberCoders; Pittsburgh (Allegheny County); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Vacation/PTO

Medical

Dental

Vision

401k

Bonus

Job summary

CyberCoders seeks a Senior Machine Learning Engineer to lead the design, development, and deployment of advanced ML systems with a focus on generative modeling. You will build and optimize generative models, write production-grade Python code, and collaborate with cross-functional teams including researchers, software engineers, and product owners.

The role emphasizes production-ready pipelines, scalable infrastructure, and MLOps practices to ensure robust, maintainable solutions across multiple

Qualifications

• 5+ years of ML experience building production systems.
• Proficiency in Python and ML libraries.
• Experience with generative modeling (VAEs, GANs, diffusion).

Responsibilities

• Design and optimize generative models for real-world use.
• Produce production-grade Python code and reusable components.
• Lead end-to-end ML projects from data prep to deployment.
• Collaborate with researchers and domain experts.
• Build data pipelines and scalable infrastructure.
• Deploy and monitor models with MLOps.
• Tune performance and optimize inference latency.
• Mentor junior engineers and uphold reproducible research.
• Document experiments and deployment procedures.

Skills

Python

ML frameworks

Generative models

MLOps

Data pipelines

Tools

Docker

Kubernetes

Spark

Airflow

CI/CD

Job description

Senior Machine Learning Engineer

Onsite Tues, Wed, Thurs in one of these locations:
Durham, NC; NYC, NY; or Pittsburgh, PA

Position Overview

We are seeking a Senior Machine Learning Engineer to lead the design, development, and deployment of advanced machine learning systems with a focus on generative modeling. The role combines research-quality model development and production-grade software engineering: you will build and optimize generative models, implement robust Python code and data pipelines, collaborate with cross-functional teams (research scientists, software engineers, product owners), and help drive ML best practices across the organization. Experience applying ML to biomolecular problems is a strong plus.

Key Responsibilities

• Design, implement, and optimize state-of-the-art generative models (e.g., VAEs, GANs, diffusion models) for real-world applications.
• Write production-quality Python code, develop reusable model components, and maintain clean, well-tested repositories.
• Lead end-to-end ML projects: data preprocessing, model training, hyperparameter tuning, evaluation, and deployment.
• Collaborate closely with research scientists and domain experts to translate scientific objectives into scalable ML solutions.
• Build and maintain data pipelines and infrastructure to support large-scale training and inference workloads.
• Deploy and monitor ML models in production using MLOps best practices (CI/CD, containerization, monitoring, and rollback).
• Profile and optimize model performance and inference latency for CPU/GPU environments, including mixed-precision and model compression techniques.
• Mentor and review code for junior engineers, contribute to team standards, and evangelize reproducible research practices.
• Document models, experiments, and deployment procedures to ensure cross-team transparency and knowledge transfer.

Qualifications

• Strong background in Machine Learning with 5+ years of industry or research experience building ML systems.
• Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, JAX) and ecosystem tools.
• Demonstrated experience designing and training Generative Modeling architectures (VAEs, GANs, diffusion models, autoregressive models, etc.).
• Solid understanding of core ML fundamentals: probability, optimization, representation learning, and model evaluation.
• Experience deploying ML models to production, familiarity with MLOps tools and workflows (Docker, Kubernetes, CI/CD, model monitoring).
• Proven software engineering skills: version control, testing, code reviews, and clear documentation.
• Experience working with large datasets, feature engineering, and scalable data pipelines (Spark, Airflow, or similar) is preferred.
• Strong communication skills and experience collaborating with cross-functional teams to deliver measurable results.
• Nice to have: experience with Biomolecular Simulation or applying ML to molecular/biophysical problems.
• Nice to have: experience with cloud platforms (AWS, GCP, or Azure) and GPU-accelerated training environments.

Benefits

• Vacation/PTO
• Medical
• Dental
• Vision
• 401k
• Bonus

Relocation