Acclaim Technical Services, founded in 2000, is a leading language and intelligence services company supporting a wide range of U.S. Federal agencies. We are an Employee Stock Ownership Plan (ESOP) company, which is uncommon within our business sector. We see this as a significant strength, and it shows: ATS is consistently ranked as a top workplace among DC area firms and continues to grow. We are actively hiring a Machine Learning Engineer with TS/SCI clearance and polygraph with an entrepreneur’s mindset to support a fast-paced and growing data science team working on projects critical to National Security in McLean, Virginia. You will perform network analysis to produce actionable leads in support of CT operations. RESPONSIBILITIES The Machine Learning Engineer will work with a cross-functional team of Data Scientists to build state-of-the-art models, automate data pipelines, and develop sustainable data structures for warehousing. The Engineer will engage their soft-skills, communicating complexity to non-technical audiences, mentoring junior teammates, and finding consensus for critical decisions. No two days are alike. The Machine Learning Engineer will react to real-world events to obtain the best possible assessments for the policymakers and warfighters who need them. At the same time, the Engineer will be planning, testing, and building the ML-driven apps which will accelerate the customer’s capabilities. The Engineer will work in conditions of technical scarcity that often accompany a classified development environment. They will find workarounds and advocate for new approaches while continuing to drive forward. They will step out of your comfort zone. On a given day, the Engineer could brief a simple OLS to a member of Congress or fine-tune the latest transformer model. On some days, the Engineer may do the work of a Software Engineer, a Social Science Statistician, or a Cloud Architect. The Engineer will be trained in our subject matter, which is inherently human-focused and, therefore, messy. The Machine Learning Engineer who will succeed in this position is a master ML practitioner and an engineer at heart. We are looking for a thought leader with an impulse to experiment and build. Our ideal candidate will be flexible and adaptable, embracing setbacks and changing requirements with humor and expert-level proficiency. Teamwork and soft skills are critical to success in this position. The ideal candidate will enjoy working as a member of a tight-knit and hierarchically flat team. The Machine Learning Engineer will jump in to help debug a script without asking, and take an hour to walk a junior teammate through the finer points of hyperparameter selection.
REQUIRED EDUCATION & EXPERIENCE Must possess an active TS/SCI clearance with polygraph
Extensive experience deploying machine learning models into production in a cloud based environment (AWS preferred) 
At least 2 years direct experience developing and deploying deep learning models
Expert-level proficiency in at least one scripting language and a desire to code primarily in Python
Extensive experience with the SciPy stack or similar ML framework
Experience with at least one modern deep learning framework (ie TensorFlow, PyTorch)
Experience deploying code in containerized environments using Docker and at least one container orchestration service
Experience designing, developing, and deploying a wide array of data architectures, including data warehouses, data lakes, and standalone relational databases
Must be a resilient problem-solver, adept at finding creative solutions given limited resources and tight deadlines
Typically requires BS degree and 12-15 years of prior relevant experience or Masters with 10-13 years of prior relevant experience.​​​​​​​ DESIRED EXPERIENCE Full stack development experience; including preference for modern java script frameworks such as React, Angular, or Vue
Proficiency working with transformer and LSTM-based NLP models
Technical leadership experience with a track record of quality, on-time deployment
Experience with NoSQL and graph database architectures