Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Machine Learning Research Engineer (MLRE) - Workflows/Systems
San Francisco Office · New York Office · San Francisco, California, United States
Open 5 months, first seen by us 155 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Machine Learning — team source
Department: Machine Learning
From the employer’s posting
What you’ll work on
Full postingWe're looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed.
Build and maintain multi-stage asynchronous workflows for data generation, training and evaluation
Rationalize machine learning systems design and software architecture
Identify blockers and build solutions that scale to the size of foundation models
Act as the glue between research scientists and the infrastructure team
From the employer’s posting
We're looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.
Build and maintain robust multi-stage asynchronous workflows for running data generation, training, and evaluations for our machine learning stack.
Rationalize machine learning systems design and software architecture.
Identify blockers and build solutions that scale to the size of foundation models.
Operate as the glue between research scientists and the infrastructure team.
What you’ll bring
All qualificationsCore experience
- Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery.
- Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc.
Qualification wording
Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery.
Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc.
Posting record
Report an errorOpen 5 monthsWe first saw this posting 155 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026155 days ago
- Recorded sightings
- 69
- Last seen by us
- Nov 5, 2026
- Employer says posted
- Apr 29, 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 155
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,418 similar postings that came down. Coming down is not always a hire.
Achira is hiring
5 open jobs
- No new data jobs since July
- Its jobs tend to stay up a long time
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.
- Dagster
- PyTorch
Where the posting names them
- Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery. - Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc. - Lack of fear around interacting with quantum chemical scientists and their data pipelines.
- At least two years relevant industry experience. - Highly fluent in and enthusiastic about PyTorch and JAX. - Used to thinking in asynchronous primitives.
Your next step
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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- No account needed. “Autofill from resume” at the top of the form fills the key fields from your file.
Already applied? Track this application
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
Why Achira
At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.
At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.
About the Role
We're looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.
What You’ll Do
Build and maintain robust multi-stage asynchronous workflows for running data generation, training, and evaluations for our machine learning stack.
Rationalize machine learning systems design and software architecture.
Identify blockers and build solutions that scale to the size of foundation models.
Operate as the glue between research scientists and the infrastructure team.
About You
At least two years relevant industry experience.
Highly fluent in and enthusiastic about PyTorch and JAX.
Used to thinking in asynchronous primitives.
Strong views on library design: clean abstractions, minimal surface area, consistency.
Solid track record of observable artifacts (e.g., GitHub) showing clear, well-documented code.
ML generalist who knows what scalable, reliable ML systems look like.
Nice to Have
Even if you hit none of these bonus features, we encourage you to apply!
Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery.
Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc.
Lack of fear around interacting with quantum chemical scientists and their data pipelines.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
We're looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models. While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities. What You’ll Do
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.