Full-Stack Software Engineer: ML Focus
Quick Summary
Design, develop, and own the production services that power our predictive, optimization, and generative AI applications using FastAPI, PyTorch, and AWS. MLOps & Infrastructure: Own deployment,
Bring modular architecture, typed interfaces, and robust testing to early-stage ML code. Write clean, maintainable, and efficient code,
At Boston Bioprocess Inc., our mission is to revolutionize biotech manufacturing through the power of AI. We are an AI-native SaaS company that builds cutting-edge software to help clients track and optimize their research, development, and manufacturing operations. We believe success is built on a foundation of technical excellence, clear communication, empathy, and a proactive, can-do attitude for solving complex challenges.
We are looking for an experienced and passionate AI-native Software Engineer to join our data science team. This is fundamentally a software engineering role with a strong machine learning focus—we need someone who designs, builds, and operates production systems, and who specializes in the software stack for building and scaling ML models.
Responsibilities
~1 min read
- →Production ML: Design, develop, and own the production services that power our predictive, optimization, and generative AI applications using FastAPI, PyTorch, and AWS.
- →MLOps & Infrastructure: Own deployment, retraining workflows, and establish best practices for versioning, CI/CD, observability, and drift monitoring. Be judicious in balancing supporting immediate needs with long-term infrastructure requirements.
- →Engineering Rigor: Bring modular architecture, typed interfaces, and robust testing to early-stage ML code. Write clean, maintainable, and efficient code, taking ownership of the quality and performance of the ML systems you build.
- →Backend & Data Connectivity: Create robust REST APIs, ETL pipelines, and human-in-the-loop feedback systems.
- →Cross-Functional Collaboration: Work closely with data scientists and wet-lab scientists to translate scientific requirements into production constraints.
- →Agile & AI-Assisted Development: Actively integrate AI-powered tools into your daily workflow to optimize and speed up tasks such as coding, refactoring, testing, and documentation. Effectively use Agile practices to plan and track your work.
Requirements
~1 min read
- Education & Experience: Master’s degree in Computer Science, Engineering, or a related field from a reputed US/European/APAC University with 2+ years of proven experience as a Full-Stack Developer (or similar role), OR a Bachelor’s degree from a Tier 1 Indian University with 5+ years of experience in a related field.
- Frontend Expertise: Deep expertise in building complex, UI-heavy, and responsive web applications using React.js, Next.js, Svelte, etc.
- Backend Knowledge: Strong knowledge of Python-based back-end languages such as FastAPI, Django, or similar.
- Data & Databases: Experience with databases like MySQL, PostgreSQL, or similar, including schema design for experimental or scientific data.
- ML in Production: Hands-on experience integrating ML frameworks (PyTorch, Jax, Tensorflow, or similar) into production software, with a clear understanding of how inference, batching, and model lifecycle behave under real load.
- Cloud & DevOps: Proven experience deploying and maintaining services and ML models on cloud platforms—AWS (SageMaker, ECS, Lambda), GCP, or similar—alongside proven experience with DevOps practices and CI/CD pipelines.
- Core Soft Skills: Strong communication, excellent problem-solving skills and attention to detail.
- Experience building LLM-powered applications (prompt engineering, evaluation, caching, agentic workflows).
- Experience with Bayesian methods, optimization under uncertainty, or recommender systems.
- Exposure to laboratory informatics systems (LIMS) or Manufacturing Execution Systems (MES), or similar.
What We Offer
~1 min readLocation & Eligibility
Listing Details
- First seen
- September 26, 2026
- Last seen
- October 5, 2026
Posting Health
- Days active
- 8
- Repost count
- 0
- Trust Level
- 34%
- Scored at
- October 5, 2026
Signal breakdown
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