Senior Machine Learning Engineer

Business Alliance HR is recruiting a Senior Machine Learning Engineer for a High-tech company working in the Biotechnology, computational biology, and applied AI Industry, located in Jordan.

Employment Type: Permanent / Full-time (Remotely for Jordanian-based Candidates)

The candidate should possess strong research instincts and solid hands-on engineering capabilities to join our growing team. The role sits at the intersection of theory and practice: the Senior Machine Learning Engineer will read and evaluate research literature, prototype novel approaches, and ship them into production systems that operate reliably at scale.

The work centers on predictive modeling and representation learning applied to complex, structured biological data. S/he will build models that learn meaningful representations from raw signals and translate them into accurate, reliable predictions that drive scientific and product outcomes.

1. Lead technical direction on major initiatives, mentor mid-level engineers, and shape the team's technical roadmap.
   
2. Drive architectural decisions for machine learning systems and establish best practices around experimentation, evaluation, and reproducibility.
   
3. Design, build, and deploy predictive models end-to-end — from data exploration through production deployment and ongoing monitoring.
   
4. Develop and apply representation learning techniques (embeddings, self-supervised learning, contrastive methods, dimensionality reduction) to extract meaningful structure from complex datasets.
   
5. Stay current with research literature and translate promising ideas from publications into working prototypes and, where appropriate, production-grade systems.
   
6. Own the full machine learning lifecycle: data pipelines, feature engineering, model training, evaluation, deployment, and ongoing performance monitoring.
   
7. Collaborate closely with product, data, and domain experts (including computational biologists) to scope problems, define success metrics, and deliver measurable improvements.
   
8. Write clean, maintainable, production-grade code and contribute to shared machine learning infrastructure.
   

1. Track record of leading complex, ambiguous machine learning projects from problem definition through production.
   
2. Deep expertise in at least one area of predictive modeling or representation learning.
   
3. Experience mentoring engineers and influencing technical decisions across teams.
   
4. Comfort making architectural trade-offs and pushing back on unclear requirements.
   
5. Strong theoretical foundation in machine learning, including the mathematical underpinnings of predictive modeling, optimization, probability, and linear algebra.
   
6. Demonstrated experience with representation learning techniques (embeddings, self-supervised learning, autoencoders, contrastive learning, or related approaches).
   
7. Proven track record of building predictive models (regression, classification, structured prediction) on real-world data, with rigorous evaluation practices.
   
8. Hands-on experience implementing machine learning systems in Python, with proficiency in frameworks such as PyTorch, TensorFlow, or JAX, alongside the standard data stack (NumPy, pandas, scikit-learn).
   
9. Ability to read, critically evaluate, and implement ideas from research papers.
   
10. Solid software engineering fundamentals: version control, testing, code review, debugging, and performance profiling.
    
11. Experience taking models from prototype to production, including deployment patterns, monitoring, and managing distribution shift.
    
12. Excellent English communication skills (written and verbal) are a big plus. The candidate is expected to discuss complex technical concepts clearly, produce high-quality documentation, and collaborate effectively with an English-speaking team.
    

1. Exposure to biology, computational biology, bioinformatics, or related life sciences (through prior work, research, or coursework) is a plus but not required.
   
2. Familiarity with biological data modalities such as sequencing data, omics, protein and molecular structure, or biomedical imaging.
   
3. Publications, open-source contributions, or technical blog posts demonstrating depth in a machine learning specialization.
   
4. Experience with distributed training, model optimization, or large-scale inference.
   
5. Familiarity with MLOps tooling (e.g., MLflow, Weights & Biases, Kubeflow).
   

Academic background in Machine Learning, Computer Science, or Computer Engineering (or a closely related quantitative discipline) at the Bachelor's level or above.