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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** BSL - Applied Laser Technologies LLC - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $235,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Computing, Python (Programming Language), Machine Learning, Software Deployment, SQL Databases, Management of Software Versions, Feature Engineering, Autoscaling, Large Language Models, Low Latency, Machine Learning Operations, Software Coding - **Published:** July 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=76286ffa76f863bb ## About the Role * 7+ years of engineering experience, with 5+ years building and shipping production ML/AI models. * Deep proficiency in production-grade Python and SQL, including building custom feature-engineering pipelines (not just off-the-shelf scikit-learn). Think time-decay weighting, leakage-safe k-fold cross-validation, and cascading fallback/imputation logic. * Experience training and validating gradient-boosted or ensemble estimators against strict accuracy/error tolerances, with segment-specific tuning (e.g., by category or asset type). * Experience leveraging LLMs, foundation models, and AI dev tools for both internal tooling and user-facing product use cases in production. * Experience with MLflow or a comparable tool for experiment tracking and model registry/versioning. * Comfortable owning production model-serving infrastructure on AWS - capacity planning, auto-scaling, and diagnosing memory/timeout failures at scale. * Experience with CI/CD pipelines, orchestrating production workflows, and IaC for provisioning and modifying cloud infrastructure. * Pragmatic and focused on delivering value incrementally rather than pursuing perfection. Nice-to-haves: * Experience with real-time or low-latency models serving at scale. * Previous startup experience - you understand and thrive on the pace, adaptability, and ownership required in a fast-moving environment. * Interested in or knowledgeable of trading cards, collectibles, or alternative asset markets. ## Description * Optimize our pricing models to significantly reduce infrastructure costs while maintaining and improving their accuracy, especially for high-value assets. * Iterate on our underwriting model to maximize cash advance disbursements while maintaining target risk thresholds and default rates. * Lead the full ML lifecycle from model training and feature generation to production deployment and monitoring. * Collaborate closely with our Expert Pricers to become a domain expert in the trading card market and inform model improvements. * Design and execute experiments and backtesting to discover and validate new features that improve the models' predictive power and coverage. * Own the models' AWS infrastructure, writing code for our pricing APIs to ensure the models can serve at scale and with low latency., * Shipped leaner, more accurate pricing models. You've cut infrastructure cost meaningfully while improving accuracy, especially on high-value assets. * Moved underwriting from good to great. You've iterated on the underwriting model to increase cash advance disbursements without breaching risk thresholds. * Earned trust with Expert Pricers. You're a go-to partner for the pricing team - you understand the domain deeply enough that your model changes reflect real market judgment, not just data. * Hardened the production path. The pricing APIs are faster, more observable, and easier to reason about, with monitoring in place to catch model drift or degradation before it hits customers. ## Related Videos - [Fifty Shades of Kubernetes Autoscaling](https://www.wearedevelopers.com/videos/813-fifty-shades-of-kubernetes-autoscaling) - [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) - [The state of MLOps - machine learning in production at enterprise scale](https://www.wearedevelopers.com/videos/369-the-state-of-mlops-machine-learning-in-production-at-enterprise-scale) - [Serverless-Native Java with Quarkus](https://www.wearedevelopers.com/videos/243-serverless-native-java-with-quarkus) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)