Algorithm and AI Engineer

Work Schedule

Environmental Conditions

Job Description

Thermo Fisher Scientific Inc. is the world leader in serving science with annual revenue exceeding $40 billion and extensive investment in R&D. Our Mission is to enable our customers to make the world healthier cleaner and safer. The customers we serve fall within pharmaceutical and biotech companies hospitals and clinical diagnostic labs universities research institutions and government agencies. Our innovations drive scientific breakthroughs from groundbreaking research to routine testing and real-world applications.

How will you make an impact

You will play a pivotal role in designing and delivering reliable robust AI applications algorithms and frameworks that elevate the quality and performance of our product offerings.

You will collaborate with and learn from a dedicated team of algorithm and software developers revolutionizing healthcare through low-cost and high efficiency diagnostic systems.

What will you do

• Develop prototype and deploy AI applicationsincluding chatbots copilots and autonomous agentsoptimized for safety performance and cost-efficiency.
• Build and optimize retrieval-augmented generation (RAG) pipelines (indexing chunking embedding reranking grounding).
• Implement parameter-efficient methods (LoRA/QLoRA/PEFT) and model optimization workflows.
• Prepare clean and facilitate the annotation of datasets for LLM model training and improvement.
• Apply context engineering (prompt design tool calling memory compression window strategy).
• Establish evaluation: golden sets RAG/grounding scores toxicity A/B tests latency & cost measurements.
• Integrate tools via Model Context Protocol (MCP) and other agent frameworks.
• Design and implement robust multi-turn conversational agents with clear handoff strategiessuch as transitioning to human support or fallback responsesalongside integrated safety guardrails and seamless deployment across web mobile and internal platforms.
• Operate in production: tracing prompt/version lineage drift detection incident response SLOs.
• Collaborate with cross-functional teams including software hardware and data science to ensure algorithms meet product requirements and are well-integrated into production systems.
• Contribute to code quality documentation and reproducibility ensuring algorithm reliability and transparency.
• Stay up-to-date on new technologies and industry standards to continuously enhance development and evaluation methodologies.

How will you get here

Education

Masters degree in Computer Sciences Mathematics Bioinformatics or a related field; PhD or equivalent experience is highly preferred.

Experience and skills Required

• Ideal candidates will have a minimum of 2 years of experience in AI development and evaluation. Recent graduates who have conducted relevant research are also encouraged to submit their applications.
• Hands-on experience with major LLMs/APIs or frameworks (OpenAI LangChain or Anthropic Hugging Face etc).
• Strong prompt & context engineering.
• Strong programming skills in Python and familiarity with Java or JavaScript for integration tasks.
• Understanding of data preprocessing distributed training and model evaluation.
• Experience fine-tuning LLMs (i.e. GPT LLaMA Mistral) using PEFT techniques like LoRA/QLoRA.
• Strong communication skills and the ability to present work to both technical specialists and non-experts.
• Ability to work both independently and collaboratively.
• Highly motivated and fast learner: able to proactively scope ambiguous tasks set clear goals ship small iterations seek feedback and reliably follow through.

Preferred

• Familiarity with RAG frameworks with vector search (e.g. FAISS/pgvector/Pinecone) hybrid search rerankers citation/grounding.
• Experience with MLOps tools (MLflow Kubeflow Docker CI/CD) and cloud services.
• Hands-on experience in building agents or chatbots.
• Evaluation: RAGAS/G-Eval or similar; offline/online metrics and A/B frameworks.
• Data systems: SQL/NoSQL message queues object storage; schema design for documents & metadata.
• Understanding of AI safety principles hallucination mitigation and factuality evaluation techniques.
• Hands-on experience with AWS SageMaker Bedrock and Step Functions along with other relevant AWS services to build deploy and orchestrate AI agents in scalable production-grade workflows.
• Experience in the biotechnology industry is a plus.