Senior Machine Learning Engineer

### Who you are
- 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
- 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
- Even if you don’t meet 100% of the above job description you should still seriously consider applying. Studies show that you can still be considered for a role if you meet just 50% of the role’s requirements

### What the job involves
- 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
- 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
- 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
- What Success Looks Like (6–12 Months)
- Deliver 2 high-impact ML capabilities with measurable customer value
- Establish standardized experimentation, monitoring, and RCA workflows
- Introduce GenAI/agentic capabilities that improve productivity and insight generation
- Reduce time-to-production and increase reuse across DS teams
- Drive measurable improvements in model performance and system reliability