Evlo AI is hiring a Machine Learning Engineer in Boston, Massachusetts to own the design, training, and production deployment of ML systems behind core product features — recommendation, ranking, and predictive models serving high-traffic, low-latency environments. You will join a well-funded applied ML team that treats models as software: versioned, tested, monitored, and improved over time. The data is messy, the problems are real, and model quality directly drives revenue. Expect to work with Python, PyTorch, scikit-learn, PySpark, SQL, and AWS (SageMaker, ECS, Lambda), and to ship with proper canary rollouts, monitoring, and rollback plans. The role asks for 3–7 years of ML engineering experience with multiple models shipped to production. Compensation was not published in the listing.
About the Team — Applied ML at Evlo AI
Company: Evlo AI — a well-funded applied machine learning team based in Boston
Product Focus: Recommendation, ranking, and predictive models behind core product functionality
Engineering Culture: Models treated as software — versioned, tested, monitored, continuously improved
Environment: High-traffic, low-latency serving where model quality directly affects revenue
Tooling: MLflow experiment tracking, CI/CD for models, reusable training pipelines, Prometheus and Grafana monitoring
Career Opportunity — ML Engineering in Boston
Real Impact: Your models power product features that users and revenue depend on every day
Full Lifecycle: Own design, training, deployment, monitoring, and iteration instead of handing work off
Modern Stack: PyTorch, PySpark, SageMaker, Docker, Kubernetes, and MLOps tooling in daily use
Career Growth: Pathway to Senior ML Engineer, Staff ML Engineer, or ML Platform Lead
Position Overview
This Machine Learning Engineer role at Evlo AI is built for someone who likes shipping models, not just training them. You will design, train, and productionise ranking, classification, and forecasting models using Python, PyTorch, and scikit-learn, build and maintain feature pipelines in PySpark and SQL so training and online serving stay in sync, and deploy to AWS using SageMaker, ECS, and Lambda with versioning, canary rollouts, and rollback strategies. Once models are live, you instrument monitoring for data drift, latency, and performance regressions with Prometheus, Grafana, and automated alerts, and tune inference through quantisation, batching, and distillation where latency budgets demand it. You will partner with data engineers and product managers to turn business questions into well-scoped ML problems with measurable success criteria, and contribute to shared infrastructure such as MLflow experiment tracking, CI/CD for models, and reusable training pipelines. Production-quality, tested code is expected, not notebook-only work.
Why This Role Matters: In a high-traffic product, a small gain in ranking quality or a faster, cheaper inference path shows up directly in revenue and user experience. As ML Engineer, you close the gap between a promising experiment and a reliable production system that stays accurate as data changes.
Key Responsibilities
Model Design, Training & Productionisation
- Design, train, and productionise ranking, classification, and forecasting models using Python, PyTorch, and scikit-learn
- Translate business problems into well-scoped ML problems with measurable success criteria
- Work with messy real-world data and design sound evaluation for model quality
- Ship models as software: tested, versioned, and ready for review
Feature Pipelines & Serving Parity
- Build and maintain feature pipelines in PySpark and SQL
- Ensure parity between training and online serving environments
- Collaborate with data engineers on data quality and pipeline reliability
- Contribute reusable training pipelines that other engineers can build on
Deployment, Monitoring & Performance
- Deploy models on AWS (SageMaker, ECS, Lambda) with versioning, canary rollouts, and rollback strategies
- Instrument monitoring for data drift, latency, and performance regression using Prometheus, Grafana, and automated alerting
- Optimise inference with quantisation, batching, and model distillation when latency budgets require it
- Respond to alerts and improve models continuously after launch
Shared ML Infrastructure
- Contribute to experiment tracking with MLflow
- Help build CI/CD for models and reusable training pipelines
- Partner with product managers so ML work stays tied to real business outcomes
- Share practices that raise engineering quality across the ML team
Qualifications & Requirements
Experience
- 3–7 years in machine learning engineering, applied ML, or a closely related role, with multiple models shipped to production
- Strong Python engineering — production-quality, tested code, not just notebooks
- Deep hands-on experience with PyTorch or TensorFlow and modern ML tooling such as MLflow, Airflow, or Kubeflow
Infrastructure & Fundamentals
- Production experience with AWS or GCP ML infrastructure, including containerised serving with Docker and Kubernetes
- Solid ML fundamentals: evaluation design, feature engineering, regularisation, and handling class imbalance and skewed data
- MS or PhD in Computer Science, Statistics, Applied Math, or equivalent practical experience
Nice to Have
- LLM fine-tuning and serving experience (vLLM, LoRA)
- Real-time streaming systems such as Kafka or Flink
- Recommendation systems at scale
About Evlo AI — Boston, Massachusetts
Evlo AI is a well-funded applied machine learning team in Boston that builds recommendation, ranking, and predictive systems for core product use. The team’s working principle is simple: treat models as software. That means version control, testing, monitoring, and steady improvement, backed by shared infrastructure like MLflow tracking and CI/CD for models. Engineers here work close to product and data teams on problems where data is imperfect and the results are measured in real revenue. For ML engineers who want ownership from experiment to production, this is a hands-on environment with a modern stack and clear business stakes.
Career Excellence: Join Evlo AI in Boston — ship ranking and recommendation models with PyTorch and PySpark, deploy on AWS with proper monitoring, and build a lasting career in production machine learning.
Who Should Apply?
- Applied ML Engineers: With several production models behind them and a bias toward reliable shipping
- MLOps-Minded Data Scientists: Ready to own deployment, monitoring, and rollback, not just modelling
- PyTorch and PySpark Practitioners: Comfortable with feature pipelines and training/serving parity
- AWS ML Engineers: Experienced with SageMaker, ECS, Lambda, Docker, and Kubernetes
- Recommendation and Ranking Specialists: Who enjoy latency-sensitive, revenue-linked problems
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