Sr Machine Learning Engineer

project44•Chicago, IL

4d•Hybrid

About The Position

project44 is looking for a Staff Machine Learning Engineer to join our engineering team. You will work in a fast-paced Agile environment designing, building, and implementing best-in-class integrations to accelerate how project44 connects to the world’s logistics networks. About the Role Staff ML Engineerto lead the development of next-generation AI/ML systems at Project44, spanning ETA, risk, anomaly detection, and supply chain intelligence. This role sits at the intersection of applied modeling, platform thinking, and production impact, with a mandate to both ship high-value ML capabilities and build reusable Data Science platform primitives that scale across teams. In addition, this role will drive the integration of Generative AI, LLMs, and agentic systems into core workflows—enabling reasoning-driven diagnostics, automation, and intelligent decision support across the supply chain. You will work closely with Data Science, ML Engineering, Data Engineering, Platform, and Product teams to deliver production-grade systems and establish a scalable, repeatable approach to AI development at P44.

Requirements

Experience 5+ years in Data Science / Applied ML with a strong track record of building and deploying production-grade ML systems.
Core Modeling & Technical Expertise Deep expertise across tree-based models, transformers, probabilistic modeling, and feature engineering, with a strong data-centric mindset.
Data & Platform Fluency Proficient in SQL and Python, with hands-on experience in modern data platforms (Snowflake/Databricks), pipelines (Spark, Airflow), streaming systems (Kafka), and MLOps tooling.
GenAI & LLM Capability Experience building RAG systems, working with embeddings and vector databases, and developing LLM-based applications and agentic workflows. Strong understanding of evaluation, guardrails, and safe deployment of GenAI systems.
Systems & Engineering Mindset Familiarity with distributed systems, APIs, and deployment patterns, with the ability to write clean, production-quality code.
Analytical Rigor & Diagnostics Strong ability to evaluate model performance, detect edge cases, run root cause analysis, and design robust monitoring and evaluation frameworks.
Data Quality & Signal Awareness Experience handling messy, real-world data, including drift, bias, missingness, and anomalies, along with tools and techniques to detect and address these issues.
Modeling Judgment & Trade-offs Ability to identify when to use ML versus heuristics and design pragmatic hybrid solutions. Strong understanding of architectural trade-offs across models and systems.
Business Impact Orientation Clear understanding of how ML metrics translate to business outcomes, with the ability to balance trade-offs across accuracy, scalability, latency, and customer experience.
Leadership & Communication Strong problem framing, stakeholder influence, and ability to communicate model behavior and decisions clearly to both technical and non-technical audiences. Proven track record of driving measurable business impact.

Nice To Haves

Experience in logistics (Ocean, Truckload), supply chain, or high-volume operational systems
Experience with geospatial data, routing, and tracking systems
Experience with anomaly detection, fraud modeling, ETA prediction
Experience building internal platforms or tools for DS teams

Responsibilities

Drive High-Impact ML Systems Lead end-to-end development of models for ETA, risk, anomaly, and fraud—leveraging advanced techniques (embeddings, transformers, hybrid models).
Build Data Science as a Platform Develop reusable ML infrastructure (features, experimentation, deployment, monitoring) to scale model development and reduce time-to-production.
Lead GenAI & Agentic Systems Build LLM-powered solutions (RAG, diagnostics, automation, coding agents) and establish guardrails, evaluation, and explainability.
Translate Business Problems into ML Solutions Convert customer workflows into well-defined ML problems and ensure measurable business impact.
Drive Experimentation & Evaluation Establish strong offline/online evaluation frameworks tied to business outcomes.
Collaborate Across Engineering Partner with MLE and DE to build scalable, reliable systems across the ML lifecycle.

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