About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Computer Software.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

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What You’ll Do

You will design, develop, and productionize machine learning and AI-driven features that solve real-world problems. Working on a collaborative engineering team, you’ll turn research and data into scalable, maintainable software.

• Design, train, validate, and deploy machine learning models (supervised, unsupervised, and deep learning) for product features.
• Implement data pipelines and feature engineering processes to support model development and evaluation.
• Collaborate with product managers, data scientists, and engineers to define requirements and measure impact.
• Productionize models using best practices for observability, monitoring, CI/CD, and model versioning.
• Write clean, well-tested code and contribute to code reviews and architecture discussions.
• Optimize model performance and inference latency for large-scale systems.
• Investigate and remediate model drift, bias, and data-quality issues.
• Communicate results, technical trade-offs, and roadmap items to stakeholders.

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What We’re Looking For (Required Qualifications)

• 3+ years of professional experience building and deploying machine learning or AI systems.
• Strong programming skills in Python and experience with ML libraries such as TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of machine learning fundamentals (model training, evaluation metrics, regularization, hyperparameter tuning).
• Experience with data engineering tools (SQL, dataframes, ETL) and working knowledge of large datasets.
• Practical experience deploying models to production environments and familiarity with containerization (Docker) and orchestration (Kubernetes) concepts.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and managed ML services.
• Good communication skills and ability to work cross-functionally with product and engineering teams.

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Nice to Have (Preferred Qualifications)

• MS or PhD in Computer Science, Machine Learning, Statistics, or related field.
• Experience with NLP, computer vision, or time-series models.
• Background in distributed training, model parallelism, or GPU-accelerated workflows.
• Experience with feature stores, model monitoring platforms, or MLOps tooling (MLflow, Seldon, KFServing, TFX).
• Familiarity with secure-by-design development practices and data privacy regulations.

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Why Join (What We Offer)

• Opportunity to build and ship AI/ML features that impact real customers at scale.
• Collaborative team culture with mentorship and technical growth opportunities.
• Competitive benefits and flexible work arrangements (subject to client policy).
• Access to cutting-edge tooling, cloud resources, and training budget for continuous learning.

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Interview & Hiring Notes (Recruiter Best Practices)

• Expect a technical screen focused on coding and ML fundamentals, a system design / architecture discussion, and a final loop that includes applied ML casework and behavioral interviews.
• Provide examples of past ML projects, code samples or notebooks, and any relevant model evaluation metrics.
• If invited, be prepared to discuss trade-offs you made in prior model deployments (latency vs. accuracy, cost, observability).

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Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.