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Full Stack Machine Learning Engineer Jobs (NOW HIRING)

BigHat

$150K - $200K/yr

Machine Learning Engineer Department: DS/ML (Data Science/Machine Learning) Employment Type ... At BigHat Biosciences our full-stack antibody drug development platform uses AI/ML to drive every ...

Givzey

Machine Learning Engineer

Machine Learning Engineer We're looking for a talented and motivated Machine Learning Engineer to ... Tackle a wide variety of technical problems throughout the stack and contribute daily to all parts ...

BigHat

Machine Learning Engineer

San Mateo, CA · On-site

$150K - $200K/yr

Machine Learning Engineer Department: DS/ML (Data Science/Machine Learning) Employment Type ... At BigHat Biosciences our full-stack antibody drug development platform uses AI/ML to drive every ...

Quantum Machines

Machine Learning Engineer

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning ... control stack, including QUA, Qualibrate, and the OPX1000. * Work directly with customers and ...

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... Familiarity with data processing stacks such as Spark and Airflow. * Experience with multi-node GPU ...

Hadrian Automation

Machine Learning Engineer

Torrance, CA · On-site

$160K - $300K/yr

By combining AI, advanced software, robotics, and full-stack manufacturing, we help aerospace and ... As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling ...

Kindredventures

Machine Learning Engineer

Seattle, WA · On-site

$120K - $140K/yr

The role We're adding an Machine Learning Engineer to our team to help us build and scale our core ... The tech stack Our backend tech stack is based on Python, GCP, Go, protobufs, BigQuery and MySQL.

Grid

Machine Learning Engineer

Seattle, WA · On-site

$120K - $140K/yr

The role We're adding an Machine Learning Engineer to our team to help us build and scale our core ... The tech stack Our backend tech stack is based on Python, GCP, Go, protobufs, BigQuery and MySQL.

Grid

Machine Learning Engineer

Seattle, WA · On-site

$120K - $140K/yr

The role We're adding an Machine Learning Engineer to our team to help us build and scale our core ... The tech stack Our backend tech stack is based on Python, GCP, Go, protobufs, BigQuery and MySQL.

Daman Inc

Machine Learning Engineer

San Antonio, TX · On-site

Experience with AWS or Azure ML stack is desired * Experience in using Python, R or Matlab is required * Industry experience as a Machine Learning Engineer * Knowledge of or experience in building ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a ... We offer a full comprehensive benefits package including medical, dental and vision. Employees ...

aqua IT

Machine Learning Engineer

Herndon, VA

Develop and maintain full-stack applications while ensuring operational excellence and reliability ... of machine learning/statistical modeling data analysis tools and techniques Preferred ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a ... We offer a full comprehensive benefits package including medical, dental and vision. Employees ...

Redolent, Inc.

Sr. Full Stack Engineer

Irving, TX · On-site

Job Title: Sr. Full Stack Engineer Location: Irving, TX (Preferred) | Minneapolis, MN | Chandler ... Experience developing machine learning solutions using diverse datasets, including AI model ...

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Full Stack Machine Learning Engineer information

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$44.5K

$134.8K

$190.5K

How much do full stack machine learning engineer jobs pay per year?

As of Sep 14, 2026, the average yearly pay for full stack machine learning engineer in the United States is $134,771.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,000.00 and $158,000.00 per year, depending on experience, location, and employer.

What is a full stack machine learning engineer?

A Full Stack Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models into production. They work across the entire ML pipeline, from data collection and preprocessing to model training, evaluation, and deployment using backend and frontend technologies. This role requires expertise in software engineering, data engineering, and machine learning frameworks like TensorFlow or PyTorch. Additionally, they ensure scalability, reliability, and maintainability of ML systems in real-world applications.

What are the key skills and qualifications needed to thrive as a full stack machine learning engineer?

To thrive as a Full Stack Machine Learning Engineer, you need robust programming skills (Python, JavaScript), a deep understanding of machine learning algorithms, and experience with both backend and frontend development. Familiarity with frameworks like TensorFlow or PyTorch, cloud platforms (AWS, Azure, GCP), and tools such as Docker and Kubernetes, as well as relevant certifications, are highly beneficial. Strong problem-solving abilities, effective communication, and a collaborative mindset are essential soft skills for working across interdisciplinary teams. These competencies are crucial to designing, deploying, and scaling machine learning solutions in production environments while ensuring seamless integration from data to user interface.

What are some typical challenges full stack machine learning engineers face, and how do they overcome them?

Full Stack Machine Learning Engineers often encounter challenges such as integrating complex machine learning models into scalable and maintainable production systems, and ensuring efficiency across both backend and frontend components. They must address issues like managing large and varied datasets, optimizing model inference times, and adapting to fast-evolving technologies. Overcoming these hurdles often requires close collaboration with data scientists, DevOps professionals, and product teams, as well as staying updated with best practices in MLOps and system architecture. Being proactive in learning new tools and fostering effective communication are key strategies for success in this dynamic role.

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Full Stack Machine Learning Engineer jobs near you
Infographic showing various Full Stack Machine Learning Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $134,771 per year, or $64.8 per hour.

Machine Learning Engineer

On-site

BigHat
See more jobs at BigHatexternal

$150K - $200K/yr

Other

Posted 9 days ago

Job description

Machine Learning Engineer

Department: DS/ML (Data Science/Machine Learning)

Employment Type: Full Time

Location: San Mateo, CA


Description

The role: We are seeking a creative, ambitious Machine Learning Scientist or Engineer to advance the state of the art in ML-driven therapeutic antibody design.

At BigHat Biosciences our full-stack antibody drug development platform uses AI/ML to drive every stage from discovery to optimization. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom LIMS++ data management and orchestration layer to automatically update and deploy the latest models. This makes development of complex, net-gen therapeutics ‘trivially parallelizable’, at a pace which only accelerates as we develop better ML tooling.

You’re not interested in just git-cloning the latest NeurIPS pub and swapping out the dataset. Motivated by an enthusiasm for the possibility of addressing unmet patient need, and a curiosity about the underlying biology, you’ll apply your top-tier ML skillset to refine and expand this state of the art protein engineering platform. Success will mean not only hands-on methods development, but actively participating in the application of our platform to the accelerated design of new drugs for devastating diseases.


Key Responsibilities
  • Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
  • Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties.
  • Develop, refine, and deploy agentic and LLM-driven optimization methods to further automate and accelerate our design-build-test loop.
  • Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
  • Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models and methods.
  • Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. - every therapeutics program at BigHat is heavily interdisciplinary.

Skills Knowledge and Expertise
  • Masters in ML/CS/EE or Bachelors with 3+ years industry experience; hands on experience developing and applying novel ML methods and a strong quantitative background.
  • Strong competency in Python, familiarity with PyTorch (even without LLMs!) and experience with modern software engineering best practices, including not just agentic/LLM-assisted coding but testing, CI/CD, etc.
  • Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
  • Energy and ambition - ready to dive into a fast-paced environment and execute across multiple projects.
  • Familiarity with the current state-of-the-art in ML-driven protein engineering
  • Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, familiarity with antibody biology and drug development, experience training and deploying models on AWS, and publications at major ML conferences.

Total Rewards

The salary estimated for this position is $150,000 - $200,000 + bonus + options + benefits. Compensation will vary depending on job-related knowledge, skills, and experience. Actual compensation will be confirmed in writing at the time of the offer.

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