Senior Machine Learning Engineer (Implementation & Scale)

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."