Staff ML Engineer

Staff ML Engineer Pay Rate: $150.00 – $200.00/hour on W2 (Rate based on experience and interview performance) Location: Austin, TX or Atlanta, GA (Hybrid 2-3 days onsite) Contract Length: 1+ Years Must be authorized to work in the United States. We are unable to sponsor or take over sponsorship of an employment visa at this time. This role is for direct W2, no C2C (corp to corp) available. Raise is currently hiring a contract team member on behalf of our client. They're expanding their team to meet growing needs, making this a unique opportunity to work with an industry leader. About the Role We are looking for a Staff ML Engineer to serve as a technical anchor on a product team building machine learning systems at scale. This is a senior individual contributor role with significant influence over technical direction, architecture, and team capability. You will drive multiple ML initiatives simultaneously, mentor less experienced engineers, and help shape how the organization approaches AI and ML development. Staff engineers here are leaders who still get hands dirty. You pair with teammates, influence architecture decisions, and own outcomes across the ML stack. Responsibilities Delivery & Execution (approx. 45% of time) Lead and contribute to the design and delivery of secure, reliable, and scalable ML solutions Partner with product managers, engineers, and designers to define and ship well-scoped features Evaluate and configure platforms and tools to meet current and future business needs Build and maintain robust monitoring, alerting, and observability tooling Technical Strategy & Planning (approx. 20% of time) Research and analyze business trends and usage signals to identify ML opportunities Evaluate and recommend technology platforms, infrastructure, and ML architectures aligned to business requirements Design infrastructure, network, database, and ML system architectures for new and evolving products Contribute to project planning across multiple concurrent initiatives Develop and deliver formal technical training for the team Continuous Learning (approx. 15% of time) Participate in internal and external ML and software engineering communities Attend conferences and technical forums; apply relevant innovations to team practice Stay current on research and tooling at the frontier of ML engineering Support & Enablement (approx. 20% of time) Serve as a senior technical resource for partner and support teams Monitor production systems and maintain Service Level Objectives Conduct regular reviews of system capacity, performance, and prediction quality Qualifications 3–6 years of relevant ML engineering experience Deep expertise in ML algorithms: clustering, forecasting, anomaly detection, neural networks Strong command of statistics and regression methods Experience with advanced ML techniques: NLP, convolutional neural networks, autoencoders, embeddings Demonstrated experience training models on very large datasets Expert-level familiarity with ML tooling: Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, TensorFlow, PyTorch Experience with GPU acceleration (CUDA, cuDNN) Experience with cloud ML platforms (e.g., Vertex AI, BigQueryML, AutoML) and data engineering at scale (BigQuery, distributed data stores) Proficiency in Python; experience with Node.js, HTML/CSS/JavaScript, React, and D3 Expert SQL skills; experience with relational and NoSQL databases Strong Git and CI/CD proficiency Deep experience in Linux/Unix environments Experience designing scalable REST APIs and web service architectures Experience with production systems design: high availability, disaster recovery, performance optimization, and security Experience with cloud automation patterns and ML managed services Experience with defensive coding and high-availability patterns Experience with A/B testing methodologies Familiarity with advanced ML architectures: GANs, GRUs, LSTMs, RNNs, CNNs, style transfer Core Competencies Strategic thinker who can connect technical decisions to business outcomes Leads through ambiguity; adapts plans as context evolves Develops others; actively builds the skills of junior and mid-level engineers Communicates with precision across all levels of the organization Drives innovation; comfortable challenging existing approaches Delivers outcomes across multiple concurrent workstreams Raise