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Machine Learning Engineer Biotech Jobs in Nevada

... machine learning models and large language models. • Conduct research to provide technical ... & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot ...

Lead Artificial Intelligence Engineer

Las Vegas, NV · On-site

$99K - $130.40K/yr

Machine Learning & Modeling * Supervised, unsupervised, reinforcement learning * Deep learning ... AI Engineering & MLOps * Model training, deployment, monitoring, and retraining * Feature stores ...

Currently, we are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/Data Scientists, and Machine Learning engineers. Who Should Apply:

Senior Engineer, Autonomy ML Systems

Las Vegas, NV · On-site

$99.80K - $136.60K/yr

Bring your passion for autonomy, robotics, and machine learning to our team to help build a ... Proven engineer with 5+ years of experience working on high-tech safety critical systems or ...

Senior Engineer, Autonomy ML Systems

Las Vegas, NV · On-site

$99.80K - $136.60K/yr

Bring your passion for autonomy, robotics, and machine learning to our team to help build a ... Proven engineer with 5+ years of experience working on high-tech safety critical systems or ...

Senior Engineer, Autonomy ML Systems

Las Vegas, NV · On-site

$99.80K - $136.60K/yr

We are seeking a talented senior engineer to contribute to evaluation of our machine learning ... Our team performs a variety of evaluations of machine learning models, including offline model eval ...

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Machine Learning Engineer Biotech information

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer in Biotech, and why are they important?

To thrive as a Machine Learning Engineer in Biotech, you need a solid background in computer science, statistics, and biology, often with an advanced degree in a related field. Experience with programming languages such as Python or R, machine learning frameworks like TensorFlow or PyTorch, and familiarity with bioinformatics tools are typically required. Strong problem-solving, communication, and interdisciplinary collaboration skills set standout candidates apart. These capabilities are crucial for developing effective models that drive scientific innovation and advance biotechnological research.

How do Machine Learning Engineers in biotech typically collaborate with research scientists and domain experts?

Machine Learning Engineers in biotech often work closely with research scientists and domain experts to translate complex biological problems into data-driven solutions. This collaboration involves regular meetings to understand experimental data, refine project goals, and iterate on model development based on domain feedback. Engineers are expected to communicate technical concepts clearly, adapt models to fit scientific needs, and help validate results alongside laboratory teams. This interdisciplinary environment fosters innovation but also requires flexibility and strong communication skills.

What does a Machine Learning Engineer do in the biotech industry?

A Machine Learning Engineer in biotech applies advanced algorithms and data analysis techniques to solve biological and medical problems. They work with large datasets such as genomic sequences, medical images, or clinical records to develop predictive models, automate data analysis, and uncover insights that can accelerate drug discovery, diagnostics, and personalized medicine. Their work often involves close collaboration with biologists, data scientists, and software engineers to create tools and solutions that improve healthcare outcomes. Machine Learning Engineers in this field need a strong background in both computational methods and biological sciences.

What is the difference between Machine Learning Engineer Biotech vs Data Scientist Biotech?

AspectMachine Learning Engineer BiotechData Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related; knowledge of ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops ML models, coding, deploying algorithms in biotech R&DAnalyzes biological data, interprets results, creates reports
Employer & Industry UsageBiotech firms, pharma companies, research labsBiotech companies, healthcare, research institutions

While both roles work with biological data, Machine Learning Engineers focus on developing and deploying ML algorithms, whereas Data Scientists analyze and interpret biological datasets to inform research and decision-making in biotech settings.

What are the most commonly searched types of Machine Learning Engineer Biotech jobs in Nevada? The most popular types of Machine Learning Engineer Biotech jobs in Nevada are:
What are popular job titles related to Machine Learning Engineer Biotech jobs in Nevada? For Machine Learning Engineer Biotech jobs in Nevada, the most frequently searched job titles are:
What cities in Nevada are hiring for Machine Learning Engineer Biotech jobs? Cities in Nevada with the most Machine Learning Engineer Biotech job openings:
AI Data Engineer - Manager

AI Data Engineer - Manager

Deloitte

Las Vegas, NV • On-site

Full-time

Posted 9 days ago


Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

60th of 138 rated financial services


Job description

Job Summary:
Deloitte is a leading consulting firm focused on transforming the nature of work through innovative solutions. They are seeking an AI Data Engineer - Manager to lead data architecture and engineering delivery for AI/ML/GenAI solutions, ensuring data integrity and scalability while managing a team and collaborating with various stakeholders.
Responsibilities:
• Lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption.
• Design and operationalize modern data and retrieval foundations to support LLM-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data.
• Manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring.
• Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
• Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases.
• Design end-to-end AI architectures, from data ingestion to model deployment, integrating with cloud and on-premises systems.
• Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
• Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
• Conduct research to provide technical solutions to scale AI/ML powered features for real-world challenges, making trade-offs based on quality, scalability, performance, and cost.
• Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
• Collaborate with Enterprise, Application, Data & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss best design.
• Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
• Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
• Be responsible for the successful execution of AI-powered applications using agile methodology.
• Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
• Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
• Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
• Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
• Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
• Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
• Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
Qualifications:
Required:
• Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems or related field.
• 6+ years of consulting experience leading delivery teams, including onshore and offshore team members
• 6+ years of experience gathering non-functional requirements and defining application architecture frameworks, including validation and testing deliverables
• 5+ years of experience working in an AI environment
• 5+ years of experience translating requirements into client ready design documents
• 5+ years of experience in software application architecture analysis, design, and delivery
• 5+ years of experience executing full system development life cycle implementations
• Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
• Limited immigration sponsorship may be available.
Preferred:
• Advanced degrees such as Masters or PhD are preferred
• Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
• 5 + years of experience in Data Science, Statistics, and Machine Learning
• 5+ years of experience in Generative AI/LLMs, preferably experienced in delivering and productionizing
• 5+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment
• 5+ year of experience in implementing cloud-based AI/ML workloads on any of AWS, Microsoft and Azure.
Company:
Deloitte is a business consulting company that offers audit, consulting, financial advisory, and tax services. Founded in 1845, the company is headquartered in London, GBR, with a team of 10001+ employees. The company is currently Late Stage.

What Deloitte employees say

Pay

Benefits

Hours and flexibility

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