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Machine Learning Engineer Biotech Jobs in New Jersey

Apex Systems

Machine Learning Engineer

Jersey City, NJ · On-site

Job#: 3049321 Machine Learning Engineer Location: Jersey City, New Jersey (Onsite) Employment Type: Contract Contract Duration: 12 Months Role Overview This position involves applying advanced ...

New

AvePoint

Senior Machine Learning Engineer

Jersey City, NJ · On-site

$190K - $220K/yr

We are seeking an analytical and innovative Senior Machine Learning Engineer to join our Data & AI team. You will play a key role in developing and deploying advanced machine learning models to solve ...

asrcfh

Senior Machine Learning Engineer

Moorestown, NJ

$103K - $141K/yr

We are currently seeking a Senior Machine Learning Engineer to join our team in Moorestown, NJ. Responsibilities: * Develops, researches, and applies machine learning, deep learning, visual ...

New

Xident B.V.

Senior Machine Learning Engineer

Union, NJ · On-site

$140 - $190/hr

Senior Machine Learning Engineer We're looking for a Senior ML Engineer to advance our age bracket classifiers and face recognition models. We run 5 binary classifiers (+12/+15/+18/+21/+25) deployed ...

New

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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All JobsMachine Learning Engineer Biotech JobsMachine Learning Engineer Biotech Jobs in New Jersey

Machine Learning Engineer Biotech information

What does a machine learning engineer do in biotech?

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.

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 are the key skills and qualifications needed to thrive as a machine learning engineer in biotech?

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.

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 New Jersey?

The most popular types of Machine Learning Engineer Biotech jobs in New Jersey are:

What are popular job titles related to Machine Learning Engineer Biotech jobs in New Jersey?

For Machine Learning Engineer Biotech jobs in New Jersey, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Biotech jobs in New Jersey look for?

The top searched job categories for Machine Learning Engineer Biotech jobs in New Jersey are:

Machine Learning Engineer Biotech jobs near you
Infographic showing various Machine Learning Engineer Biotech job openings in New Jersey as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 24% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer

5 Star Global Recruitment Partners

Newark, NJ

Full-time

Posted 4 days ago

Job description

Machine Learning Engineer

Newark, New Jersey, United States

Job Description

As a Machine Learning Engineer, you will play a pivotal role in driving the development and implementation of cutting-edge machine learning solutions for our client. Your responsibilities will encompass a wide range of tasks, from leading a small team of machine learning engineers to collaborating with cross-functional teams to deliver impactful solutions. You will be at the forefront of driving innovation and leveraging the power of machine learning to solve real-world problems, drive business growth, and create value.

Key Responsibilities

  • Lead and drive machine learning projects from inception to production: build relationships with business partners and cross-functional teams.
  • Collaborate with business leaders, subject matter experts, and decision-makers to develop success criteria and optimize new products, features, policies, and models.
  • Partner with data scientists to understand, implement, train, and design machine learning models.
  • Collaborate with the infrastructure team to improve the architecture, scalability, stability, and performance of ML platform.
  • Construct optimized data pipelines to feed machine learning models.
  • Extend existing machine learning libraries and frameworks.
  • Develop processes, model monitoring, and governance framework for successful ML model operationalization.
  • Define objectives for the Machine Learning platform, own the technical roadmap, and be accountable for delivering results.
  • Define standards for engineering and operational excellence for running best-in-class ML platforms and continue to improve ML platforms to keep up with the latest innovations.
  • Design and implement the best architectural practices in the delivery of data science use cases.

Key Skills/Knowledge/Experience

  • 7+ years of experience in Machine Learning.
  • Extensive software engineering experience with strong working experience as a Machine Learning Engineer.
  • Bachelor's degree in computer science, computer engineering, or a related engineering field. Masters degree preferred.
  • Advanced proficiency with Python, Java, and Scala.
  • Strong computer science fundamentals such as algorithms, data structures, multithreading.
  • Experience working with Generative AI, using LangChain for Gen AI and techniques like RAG.
  • Experience using ML and DL Libraries:XGBoost, SKlearn, Tensorflow or PyTorch
  • In-depth experience building solutions using public clouds such as AWS, GCP.
  • Experience using ML platforms like SageMaker, H2O, DataRobot, etc.
  • Strong knowledge on ML model development life cycle components like containers, batch vs real time inference endpoints, application security testing etc.
  • Experience managing relationships in a cross-functional environment with multiple stakeholders.
  • Experience with developing and deploying production-grade applications with ML inferences using automation pipeline on cloud.
  • Experience working in Agile/ Scrum development process.
  • Thought leadership and innovative thinking.
  • Excellent communication and collaboration skills.

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