Pay
In line with similar jobs
$150K–$200K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Machine Learning Engineer
San Mateo, CA, San Mateo, CA
Live, first seen by us 60 days ago- Pay
$150,000–200,000/yearAnnual period assumed — pay source
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. Employment type
Read the full posting- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full Time
Employment type in the employer’s posting: full_time
From the employer’s posting
What you’ll work on
Full postingDesign generative models of antibody sequence and structure, plus property predictors
Develop multi-objective sequence optimization for lab-in-the-loop antibody design
Develop and deploy agentic and LLM-driven methods to automate the design-build-test loop
Provide ML expertise for ongoing therapeutics programs
Work with engineers to automate deployment of latest models and methods
From the employer’s posting
Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties
Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems
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
Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models and methods
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 4, 202660 days ago
- Recorded sightings
- 40
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 60
Similar jobs usually come down between day 6 and day 69.
From 4,362 similar postings that came down. Coming down is not always a hire.
BigHat Biosciences is hiring
4 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- 25% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- AWS
- PyTorch
Where the posting names them
- 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.
- 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
Your next step
Complete your application on bighatbiosciences.pinpointhq.com. The employer’s form will show what is required.
Applying through Pinpoint
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About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job 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.
Employment type
Full Time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
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. Employment type
- Location & working pattern
San Mateo, CA, San Mateo, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.