- Company Name
- Barrington James
- Job Title
- Machine Learning Engineer
- Job Description
-
Job Title: Machine Learning Engineer
Role Summary: Senior ML Engineer responsible for designing, developing, and deploying advanced machine learning models for biological datasets (genomics, imaging, experimental data) on an MLOps platform. Works remotely on a freelance/contract basis, providing technical leadership, mentorship, and cross‑functional collaboration to translate scientific challenges into production‑ready ML solutions.
Expectations: Deliver high‑quality, reproducible ML workflows; maintain model performance and lifecycle; act independently on technical projects; communicate findings to both technical and scientific stakeholders; adapt to evolving biotech data types and business needs.
Key Responsibilities:
- Lead model design, training, evaluation, and optimization for genomics, imaging, and other biotech data.
- Build and automate scalable data pipelines for structured and unstructured data.
- Develop end‑to‑end ML pipelines, including preprocessing, feature engineering, training, validation, deployment, and monitoring.
- Implement ensemble and advanced techniques to enhance predictive accuracy.
- Oversee production deployment using Kubernetes, Docker, CI/CD, and best MLOps practices.
- Mentor junior engineers and contribute to strategic planning.
- Present analytical insights clearly to technical and scientific audiences.
Required Skills:
- Strong proficiency in Python; SQL; familiarity with Scala, Java, or C++ a plus.
- Extensive experience with TensorFlow, PyTorch, scikit‑learn, and modern ML frameworks.
- Deep knowledge of MLOps (Kubernetes, Docker, CI/CD, model serving).
- Proven track record of deploying and optimizing ML models in production, preferably in biotech/healthcare/science.
- Excellent analytical, mathematical, and problem‑solving skills.
- Effective communication and cross‑functional collaboration abilities.
- Independent project management experience (freelance/contract).
Required Education & Certifications:
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Data Science, Computational Biology, Bioinformatics, or related field.
Preferred Qualifications (optional):
- Cloud experience with AWS, GCP, or Azure for ML deployment.
- Background in NLP, computer vision, or multimodal learning applied to biological data.
- Familiarity with scientific data formats (FASTQ, BAM, microscopy images).
- Experience with data visualization frameworks.