Job Closed
This listing is no longer active.
Continuous Multi-Factor Authentication Powered by Passive Biometrics. Designed for Contact Center Identity.
Machine Learning Engineer
Location
Argentina
Posted
108 days ago
Salary
$70K - $90K / year
Seniority
Senior
Job Description
Machine Learning Engineer
Twosense
• Build and maintain our production ML pipeline—including ETL processes, data cleaning, preprocessing, feature extraction, training, evaluation, deployment, and monitoring. • Develop streamlined ML workflows to effectively support our production systems. • Write clean, maintainable Python code using test-driven or test-first development practices. • Collaborate closely with founders and researchers to bring ML ideas to life—with opportunities to participate directly in research projects. • Optimize our infrastructure to handle growth and scale effectively.
Job Requirements
- Strong software engineering skills—grounded in SOLID principles and best practices.
- Hands-on experience deploying ML models to production.
- Experience with common ML libraries like scikit-learn, TensorFlow, or PyTorch.
- Basic understanding of ML fundamentals (algorithms, math/stats), along with strong intuition for how and when to apply different modeling approaches.
- Familiarity with developing and deploying ML systems using AWS tools and infrastructure.
- Experience with varied data types (structured, time-series).
- Previous experience with behavioral biometrics or security-focused products.
- Previous experience with ONNX.
Benefits
- Flexible working—remote or in-office, whatever suits you best.
- Project Day once a month—dedicate a day to something you’re passionate about.
- Equity—share directly in our success.
- Open vacation policy—take time off whenever you need.
- Subscription to online learning resources like O'Reilly and Pluralsight.
- Business English courses.
- Travel for team events and company meet-ups.
- Technical books, VPN, and high-end work equipment provided.
Related Guides
Related Job Pages
More Machine Learning Engineer Jobs
• This role is critical in making ML and AI services to power engaging user experiences • Working from end-to-end on live production services. Not just modeling, not theoretical • The work you do will directly impact our customers' day-to-day experiences • Define the best approach to solve problems with ML. Build data and model pipelines • Test and Validate services. Deploy and monitor solutions for impact • Work within a cross-functional application team to build scalable language services in the range of NLP, NLU, and NLG for live SaaS products • Help develop and groom an experiment backlog • Build models that solve real-world problems • Optimize models for production throughput and uptime requirements • Automate deployments, testing, and monitoring (MLOps)
• At Veeva Link, we're building the intelligence layer for life sciences, creating connected data applications that accelerate drug development and significantly improve patient outcomes. • We develop sophisticated applications that map and understand global scientific and medical expertise, providing high-impact insights and AI-generated intelligence. • Engineers here tackle complex data modeling, entity resolution, and advanced machine learning. • We engineer workflow applications powered by our data, using AI to find patterns and route information precisely. • This involves designing scalable pipelines and intelligent automation.
• At Veeva Link, we're building the intelligence layer for life sciences, creating connected data applications that accelerate drug development and significantly improve patient outcomes. • Develop sophisticated applications that map and understand global scientific and medical expertise, providing high-impact insights and AI-generated intelligence. • Engineer workflow applications powered by data, using AI to find patterns and route information precisely. • Define the best approach to solve problems with ML. Build data and model pipelines. • Test and validate services. Deploy and monitor solutions for impact.
• Develop large-scale distributed machine learning systems that are scalable, performant, efficient, and reliable • Collaborating with cross-functional teams to help deploy/integrate machine learning models. • Liaise with the BUs for their ML needs and work on the cross-BU ML portfolio. • Optimize feature extraction, transformation and selection. • Work with and manage. • Feature Stores for reusability across ML pipelines. • Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform • Contribute to evaluating and adopting new technologies and tools to enhance our machine-learning capabilities

