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Tensorflow Pytorch Jobs in Washington, DC (NOW HIRING)

Senior Data Scientist

Washington, DC · On-site

$134K - $196K/yr

Expert proficiency in advanced ML and AI: deep learning (TensorFlow, PyTorch, Keras), ensemble methods (XGBoost, Random Forest), Bayesian methods, and multi-objective optimization algorithms.

Expert proficiency in advanced ML and AI: deep learning (TensorFlow, PyTorch, Keras), ensemble methods (XGBoost, Random Forest), Bayesian methods, and multi-objective optimization algorithms.

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

Senior Applied Scientist

Reston, VA

$95K - $130K/yr

TensorFlow, PyTorch, MXNet etc). * Preference for a publication record in top-tier ML and NLP conferences (e.g. NeurIPS, ICML, SIGIR, ICLR, ACL, EMNLP, etc.). * Proven track record in evaluating ...

Senior Applied Scientist

Reston, VA · On-site

$95K - $130K/yr

TensorFlow, PyTorch, MXNet etc). * Preference for a publication record in top-tier ML and NLP conferences (e.g. NeurIPS, ICML, SIGIR, ICLR, ACL, EMNLP, etc.). * Proven track record in evaluating ...

Sr. Machine Learning Engineer

Fort Belvoir, VA · On-site

$118K - $162K/yr

Extensive experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras * Familiarity with cloud platforms (AWS, Google Cloud, Azure) for deploying ML solutions * Experience with ...

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Tensorflow Pytorch information

See Washington, DC salary details

$42.5K

$139K

$222.6K

How much do tensorflow pytorch jobs pay per year?

As of Jul 15, 2026, the average yearly pay for tensorflow pytorch in Washington, DC is $139,013.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,600.00 and $154,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Deep Learning Engineer specializing in TensorFlow and PyTorch, and why are they important?

To thrive as a Deep Learning Engineer with a focus on TensorFlow and PyTorch, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree. Proficiency in programming languages like Python, experience with TensorFlow and PyTorch frameworks, and familiarity with cloud platforms or GPU computing are essential. Analytical thinking, problem-solving, and effective communication are standout soft skills for collaborating with teams and interpreting model results. These skills are crucial for developing, deploying, and optimizing AI models that drive innovation and solve complex real-world problems.

What are TensorFlow and PyTorch?

TensorFlow and PyTorch are two of the most popular open-source deep learning frameworks used by researchers and developers to build, train, and deploy machine learning models. TensorFlow, developed by Google, offers robust support for production environments and has a large ecosystem. PyTorch, developed by Facebook, is known for its flexibility, ease of use, and dynamic computational graph, making it popular in academia and research. Both frameworks support a wide range of neural network architectures and are used extensively for tasks such as computer vision, natural language processing, and reinforcement learning.

What is the difference between Tensorflow Pytorch vs Data Scientist?

AspectTensorflow PytorchData Scientist
Required SkillsDeep learning frameworks, Python, machine learningData analysis, statistical skills, Python/R, machine learning
Work EnvironmentAI/ML development, research, software engineeringData analysis, reporting, business insights
Industry UsageAI/ML projects, research labs, tech companiesBusiness, finance, healthcare, tech

Tensorflow and Pytorch are deep learning frameworks used primarily by AI/ML developers, while Data Scientists utilize these tools for data analysis and modeling. Although their skill sets overlap, Tensorflow Pytorch focus on model development, whereas Data Scientists apply these models to derive insights and inform decisions.

How do TensorFlow/PyTorch engineers typically collaborate with data scientists and other team members in a production environment?

TensorFlow and PyTorch engineers often work closely with data scientists to transform experimental machine learning models into efficient, scalable production solutions. Collaboration involves frequent code reviews, shared development environments, and regular meetings to align model requirements with deployment constraints. Engineers also coordinate with DevOps teams to ensure smooth integration and monitoring of models in production. Strong communication skills and a willingness to iterate on solutions are essential for bridging the gap between research and real-world application.
What are popular job titles related to Tensorflow Pytorch jobs in Washington, DC? For Tensorflow Pytorch jobs in Washington, DC, the most frequently searched job titles are:
Senior Data Scientist

Senior Data Scientist

Unissant

Washington, DC • On-site

$134K - $196K/yr

Full-time

Posted 14 days ago


Job description

Unissant, Inc. delivers innovative capabilities to the agencies that keep our nation healthy and safe. We apply our domain expertise, data acumen, and technology know-how to achieve breakthrough results for our clients. Working collaboratively, we advance missions and careers through a focus on honesty, integrity, and dependability. We continuously look for talent, excited to join that effort. To learn more about our exciting organization, please visit us at www.unissant.com.

We are seeking a Senior Data Scientist to join our team in Washington, DC, in support of the Department of Homeland Security (DHS), Immigration and Customs Enforcement (ICE), Law Enforcement Systems and Analysis (LESA) program within the Strategy and Operations Analysis (SOA) Unit.

The SOA Unit provides advanced analytics, visualization, and modeling capabilities spanning the entire Enforcement Lifecycle to inform ERO and ICE strategic and budgetary decision-making. SOA utilizes predictive analytics, simulation, and optimization modeling to forecast resource needs, support congressional responses, and drive organizational planning at headquarters and field levels. The ideal candidate is a recognized data science leader with deep expertise in advanced ML, MLOps, operations research, and data architecture, and the ability to serve as the senior analytical voice for the SOA Unit.

Essential Duties and Responsibilities:

  • Serve as the senior technical lead for the SOA analytical program: architecting, developing, and operationalizing advanced machine learning solutions including deep learning (CNNs, RNNs, Transformers), ensemble methods, and AI-driven decision-making tools for resource allocation and prioritization problems.
  • Conceptualize, plan, design, and develop deep learning/AI algorithms for multi-objective optimization focused on ERO resource allocation, logistics, and enforcement prioritization.
  • Lead MLOps activities including model versioning (MLflow, DVC), performance monitoring, drift detection, CI/CD pipelines, and retraining workflows in production environments.
  • Prepare models for official DHS accreditation, producing documentation covering model design, methodology, requirements, decision-making use, analysis of alternatives, and stakeholder engagement.
  • Lead development, maintenance, and governance of LESA's suite of Discrete Event Simulation (DES) models; promote interoperability of products with other LESA DES models to enhance forecasting and decision-making.
  • Collaborate with data architects and enterprise architects to define scalable data architecture standards supporting analytical and AI/ML workloads; evaluate infrastructure improvements for AI/ML scalability and performance.
  • Identify gaps for LESA decision support tool development: opportunities to collect new data, improve data quality, and improve forecasting methodologies, reporting, and scenario planning capabilities.
  • Maintain and improve geospatial capabilities and Logistics Optimization Decision Support Tools; evaluate emerging AI, modeling, and BI platforms (Python, Qlik, Tableau, ArcGIS, Databricks, Palantir Foundry).
  • Develop executive-level summary reports and briefings of algorithm results using RMarkdown, Jupyter Notebook, MS PowerPoint, and MS Word for ERO/ICE senior leadership.
  • Rapidly deploy to support special projects: academic research, proof of concepts, congressional inquiry responses, policy change analysis, and ERO strategic logistics initiatives.
  • Provide technical leadership and mentorship to junior and mid-level data scientists; conduct code reviews, methodology reviews, and knowledge-sharing sessions.

Work Experience and Job Skills:

  • 10+ years of experience in data science, machine learning, or applied AI research, with significant experience supporting federal law enforcement, DHS/ICE/ERO, or national security missions.
  • Expert proficiency in advanced ML and AI: deep learning (TensorFlow, PyTorch, Keras), ensemble methods (XGBoost, Random Forest), Bayesian methods, and multi-objective optimization algorithms.
  • Demonstrated experience implementing: R libraries (h2o, Keras, mlr) and Python libraries (TensorFlow, PyTorch, mpi4py, scikit-learn); experience with MCA, PCA, and association rule mining.
  • Demonstrated MLOps experience: model versioning, monitoring, CI/CD for ML, and deployment in High-Performance Computing (HPC) and cloud environments.
  • Experience with DES modeling, operations research, and logistics optimization.
  • Deep expertise in data architecture collaboration: designing data pipelines, warehouses, and infrastructure to support large-scale ML workloads.
  • Proficiency with geospatial tools (ArcGIS, GIS platforms) and BI tools (Qlik, Tableau, Power BI, Databricks, Palantir Foundry).
  • Proven technical leadership; ability to produce executive-level deliverables using RMarkdown, Jupyter Notebook, MS Access, MS Excel, MS PowerPoint, and MS Word.

Education:

  • Bachelor's Degree required. Preferred fields: Computer Science, Data Science, Statistics, Mathematics, Operations Research, or related discipline.
  • Master's Degree or PhD in quantitative discipline (Data Science, Machine Learning, Applied Mathematics, Operations Research) strongly preferred.

Certificates, Licenses and Registrations:

  • Advanced ML/AI certifications (AWS ML Specialty, Google Professional ML Engineer, Azure AI Engineer) strongly preferred.
  • MLOps, Databricks, or HPC certifications are a plus.

Communication Skills:

  • Excellent verbal and written skills; experienced presenting complex analytical findings to senior federal leadership and non-technical audiences.
  • Ability to produce reports and briefings consistent with FOUO/LES confidentiality standards.

Clearance Requirements:

  • Active ICE clearance required; preference for candidates currently cleared or cleared within the last two years.
  • Ability to obtain and maintain required clearance level is a condition of employment.

Travel:

  • Minimal travel expected.
  • On-site in Washington, D.C. Metropolitan area.

Environmental Requirements:

  • Mainly a routine office environment.
  • May be required to lift up to ten (10) pounds.
  • Flexible in working extended hours.

The above statements are intended to describe the general nature and level of work being performed by the individual(s) assigned to this position. They are not intended to be an exhaustive list of all duties, responsibilities, and skills required. Unissant management reserves the right to modify, add, or remove duties and to assign other duties as necessary. In addition, where applicable and available, reasonable accommodation(s) may be made to enable individuals with disabilities to perform essential functions of this position.

Please note: Candidate(s) will be required to go through pre-employment screening.

Unissant, Inc. is a proud Equal Opportunity Employer! (EOE; M/F/Disability/Vets)


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