Senior Machine Learning Engineer (Implementation & Scale) at Trivelta

Boston, Massachusetts, United States -

Full Time

Start Date

Immediate

Expiry Date

13 May, 26

Salary

0.0

Posted On

12 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, C++, Go, PyTorch, TensorFlow, JAX, SQL, Spark, Vector Databases, Weights & Biases, MLflow, Kubeflow, Deep Learning, Transformers, Gradient Boosting, Feature Engineering

Industry

Description

Trivelta builds the technology that powers modern, social-first gaming experiences. Through our proprietary sweepstakes-based sportsbook and casino engine, we enable partners to launch their own fully branded, legally compliant gaming products. Our flagship consumer app, ReBet, demonstrates the power of the Trivelta platform—combining real social interactions, predictive gameplay, and casino entertainment in one unified experience. Headquartered in Boston with operations in Monterrey, Barcelona, and Atlanta, we’re scaling rapidly and building a team passionate about redefining how people play, bet, and connect online. We are looking for a Senior ML Engineer who thrives at the intersection of data science and production-grade software engineering. In this role, you won’t just be "experimenting" in a vacuum; you will be responsible for taking our proprietary datasets and transforming them into high-performance, deployed models that drive core business value. You are someone who understands that a model is only as good as the data feeding it and the infrastructure supporting it. Responsibilities: Model Implementation: Design, train, and fine-tune state-of-the-art ML models (Deep Learning, Transformers, Gradient Boosting, etc.) specifically optimized for our internal datasets. End-to-End Pipeline Development: Build and maintain robust data pipelines and training workflows to ensure reproducible and scalable model development. Optimization & Performance: Profile and optimize model latency and throughput for production environments. Data Centricity: Perform deep exploratory data analysis (EDA) to identify biases, signal-to-noise ratios, and feature engineering opportunities within our unique data silos. Collaboration: Work closely with Data Engineers to streamline data ingestion and Backend Engineers to integrate model APIs into our user-facing products. Qualifications 5+ years of professional experience in Machine Learning or Software Engineering, with at least 3 years focused on deploying models to production. Expert-level Python (and ideally C++ or Go for performance-critical components). Deep fluency in PyTorch, TensorFlow, or JAX. Experience with SQL, Spark, and vector databases (e.g., Pinecone, Milvus). Familiarity with Weights & Biases, MLflow, Kubeflow, or similar orchestration tools. Strong understanding of linear algebra, calculus, and statistics as applied to ML optimization. For example, you should be comfortable deriving or explaining loss functions like: $$L = \frac{1}{N} \sum_{i=1}^{N} (y_i - \hat{y}_i)^2 + \lambda ||w||^2$$ MS or PhD in Computer Science, Mathematics, or a related field (or equivalent "battle-tested" industry experience). Soft Skills & "Culture Fit" Pragmatism: You prefer a simple, explainable model that works today over a complex one that stays in "Research" for six months. Ownership: You don't just "hand off" code; you monitor your models in the wild and jump in when they drift. Mentorship: You enjoy leveling up junior engineers through rigorous code reviews and architectural discussions. Why Join Us? Glad you asked! ... "We don't just build models to see what’s possible; we build them to change how our industry handles data. You will have a direct seat at the table in defining our technical roadmap." This position is hybrid out of our Boston HQ located next to South Station.

Responsibilities

The role involves designing, training, and fine-tuning state-of-the-art ML models optimized for internal datasets, while also building robust, scalable data pipelines and training workflows for reproducible development. Responsibilities include optimizing model latency and throughput for production and performing deep exploratory data analysis for feature engineering.