Role Description The Senior Project Engineer will be responsible for the leadership and management of the team. Responsible for proactively managing the project’s technical requirements, schedule, contract obligations, and project communications. The Senior Project Engineer will work with the Civil Design team on transportation projects. Role will entail leading various disciplines to ensure projects get delivered on time and on budget. Typical clients include IDOT, ISTHA, CDOT and other municipalities. - Coordinate with Project Managers, Project Engineer and other discipline task leads to deliver projects - Oversee and manage subconsultants - Attend client meetings and public meetings - Mentor and delegate work to junior staff - Oversee preparation of design submittals including plans, calculations, quantities, and cost estimates - Prepare detailed scope of work and level of effort estimates - Knowledge of IDOT, ISTHA, various municipalities standards and specifications - Understand and apply client and industry engineering specifications to projects - Interact with client’s management and engineering department to facilitate project efforts Qualifications - BS in Civil Engineering or related field - 10+ years of experience - Professional Engineer License in Illinois Required - Software proficiency in Bentley MicroStation SS10 is Required, and OpenRoads Designer and AutoCAD experience are preferred - Certifications or training from IDOT and ISTHA like Documentation of Contract Quantities, Erosion Sediment, ADA Ramp, and eBuilder Preferred - Strong understanding of construction and engineering practices - Demonstrate a proactive, collaborative approach with project team members, client representative, and review agencies - Strong computer skills, specifically in the area of Microsoft Excel, Word and Outlook - Strong written and verbal communication skills - Ability to clearly communicate concepts and ideas - Excellent organizational skills with attention to detail Benefits - Competitive base pay ($130,000 - $160,000 annually) - The exact starting compensation to be offered will be determined at the time of selecting an applicant for hire, in which a wide range of factors will be considered, including but not limited to, skillset, years of applicable experience, education, credentials and licensure, as applicable. - Fully remote work schedule; based in Illinois - Medical, dental, and vision coverage plan options - Company paid life insurance and short-term/long-term disability - 401k plan with an employer safe harbor match - Paid time off - Paid company holidays - Professional development assistance, licensure, and professional membership reimbursement - Pre-tax commuter benefit - Voluntary life and accident insurance - Voluntary Critical Illness, Accident and Hospital Indemnity insurance - Pet Insurance - Identity Theft Protection

Role Description We are seeking a Machine Learning Engineer (3–5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and intelligence applications. This role spans the full ML lifecycle, from dataset development and experimentation to model training, evaluation, deployment, and monitoring. You will work both independently and collaboratively across projects involving multimodal classification systems, frontier model evaluations, model distillation research, and agentic workflows. The ideal candidate combines strong engineering fundamentals with a research mindset and enjoys tackling ambiguous, high-impact problems at the frontier of AI. You will collaborate closely with researchers, software engineers, red teamers, and subject-matter experts to develop production-ready systems that support leading AI organizations and technology companies. - Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains. - Develop and improve classification systems for safety, security, abuse detection, and intelligence applications. - Conduct experiments to benchmark, evaluate, and compare AI models, including large language models and multimodal systems. - Contribute to model distillation, optimization, and fine-tuning efforts to improve performance, efficiency, and deployability. - Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities, reliability, and safety. - Build agentic systems and automated workflows for evaluation, red teaming, research, and large-scale experimentation. - Own ML projects from initial research and prototyping through production deployment and monitoring. - Partner with software engineers to productionize ML systems and support ongoing improvements. - Provide technical expertise and guidance across client engagements and internal research initiatives. Qualifications - Brings curiosity, creativity, and rigor to ambiguous research and engineering problems, with a bias toward experimentation and rapid iteration. - Thrives in collaborative, interdisciplinary environments while also being comfortable independently driving projects to completion. - Communicates technical concepts clearly to both technical and non-technical audiences. - Is resourceful, proactive, and comfortable operating in a fast-moving startup environment. - Is excited about developing novel approaches that advance the state of AI safety, evaluation, and security. Requirements - 3–5+ years of professional experience building and deploying machine learning systems. - Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow. - Experience working across multiple modalities, with expertise in one or more of: - Computer Vision: image classification, object detection, OCR, segmentation, deepfake detection, multimodal vision-language systems, or related areas. - Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic applications, or related areas. - Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments. - Experience designing evaluation methodologies, benchmarking systems, and model performance metrics. - Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.). - Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services. - Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred. Benefits - Salary Range: $130K–$200K, depending on experience and location. - Bonus: Performance-based annual bonus. - Professional Development: Support for conferences, continuing education, or leadership training. - Work Environment: Fully remote, U.S.-based. - Health Benefits: Comprehensive health, dental, and vision coverage. - Time Off: Generous PTO and paid holiday schedule.

• Desenvolver, manter e otimizar pipelines de dados em ambiente Azure Databricks • Trabalhar com ingestão, transformação, tratamento e disponibilização de dados para consumo analítico ou operacional • Apoiar a modelagem e organização de dados em arquiteturas baseadas em Data Lake e Lakehouse • Desenvolver notebooks, jobs e rotinas de processamento utilizando Databricks • Integrar diferentes fontes de dados ao ambiente Azure, conforme necessidade do projeto • Colaborar com times técnicos e de negócio para entendimento dos requisitos e tradução em soluções de dados • Apoiar boas práticas de performance, governança, versionamento e documentação técnica

• Employee lifecycle automation: new hires, role changes, terminations, transfers, and leave. Eliminate the manual coordination that currently runs on emails and spreadsheets by building automated task orchestration with SLAs, escalation paths, and audit-ready logs. • Comp planning infrastructure hardening: access controls, versioning, approval workflows, and AI-assisted benchmarking across our SecureSheets environment. • Build a unified People analytics layer connecting ADP Workforce Now, Greenhouse, and Lattice into a governed data model that gives People leaders real workforce intelligence. • Performance and goals connectivity: build the data infrastructure that links performance data in Lattice to workforce decisions. • Spend your first 60 days embedded across every People sub-function, listening, mapping, and building a prioritized automation backlog that you own and execute against. • Establish logging, monitoring, and alerting infrastructure for all automations. Publish reusable modules to the Automation Hub. • Document all production automations to handoff-ready standard. Ensure every automation touching sensitive data has Legal sign-off and complies with HIPAA and PII governance requirements. • Additional duties and responsibilities as necessary.