Technology
Machine Learning Engineer
Build production-grade ML systems that power our clients' analytical capabilities. You will design, train, and deploy models that solve real business problems at scale.
About the Role
We are hiring a Machine Learning Engineer to join our Technology team. This is a fully remote position for someone who thrives at the intersection of applied machine learning, software engineering, and business impact.
You will work across the full ML lifecycle — from exploratory analysis and feature engineering through model training, evaluation, deployment, and monitoring. Our clients rely on these systems for demand forecasting, risk scoring, customer segmentation, recommendation engines, and more.
You will collaborate with data analysts, strategy consultants, and client engineering teams to ensure that models are not only technically sound but also integrated into real decision-making workflows.
Responsibilities
- Design, build, and deploy machine learning models for client engagements
- Develop scalable ML pipelines using modern frameworks and cloud infrastructure
- Collaborate with analysts and consultants to translate business problems into ML solutions
- Implement rigorous model evaluation, testing, and monitoring practices
- Optimize model performance for latency, throughput, and accuracy requirements
- Stay current with ML research and evaluate new techniques for practical application
- Contribute to internal tools and reusable ML components
Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related field
- 3+ years of professional experience building and deploying ML models in production
- Strong proficiency in Python and ML frameworks (scikit-learn, PyTorch, TensorFlow, or XGBoost)
- Experience with cloud ML services (AWS SageMaker, GCP Vertex AI, or Azure ML)
- Solid software engineering skills — version control, testing, CI/CD, containerization
- Understanding of statistical foundations: hypothesis testing, regression, classification, clustering
Preferred Qualifications
- Experience with NLP, time-series forecasting, or computer vision
- Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases)
- Experience with distributed computing (Spark, Dask, Ray)
- Contributions to open-source ML projects or published research
- Prior consulting or client-facing experience
Interested in this role?
Send us your resume and a brief introduction. We'll review your application and get back to you.
Compensation & Benefits
$145,000 – $190,000
Annual base salary
- Competitive base salary plus annual performance bonus
- Fully remote — work from anywhere in the US
- Comprehensive health, dental, and vision insurance
- 401(k) with 4% company match
- $3,500 annual home office and technology stipend
- $3,000 annual conference and learning budget
- Generous PTO plus flexible scheduling