H-1B1 Singapore Visa Machine Learning Engineer Jobs
Machine Learning Engineer roles qualify for H-1B1 Singapore visa sponsorship because the position meets the specialty occupation standard requiring a directly related bachelor's degree in computer science, statistics, or a related field. Singapore nationals skip the H-1B lottery entirely, and the 5,400-visa annual cap has never been exhausted.
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Get Access To All JobsYour Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), software engineers build the systems that connect generative models, scientific data, and experimental workflows into reliable, production-grade pipelines powering Lila's protein design and engineering campaigns.
We're hiring a Staff ML Engineer, Life Sciences AI to lead software infrastructure development for our protein design and engineering pipelines. This is a senior IC role focused on the engineering systems that surround and support our ML stack — pipeline orchestration, data flow between computational and experimental systems, integration of new tools and methods, and the developer experience that lets LSAI move fast on commercial partnership deliverables.
What You'll Be Building
- Architect and build software infrastructure powering Lila's protein design and engineering pipelines: orchestration, data flow, APIs, and integration with experimental systems.
- Own the engineering side of LSAI's "Lab-in-the-Loop" lifecycle — connecting computational outputs to experimental inputs and feeding results back into design workflows.
- Onboard new tools and methods developed by AI scientists and ML engineers into production-ready systems used in commercial partnership campaigns.
- Partner cross-functionally with ML researchers, scientists, and platform engineers to translate research code into reliable, scalable systems.
- Set engineering standards for LSAI software — design reviews, CI/CD, testing, observability, reproducibility — and mentor senior engineers as the team grows.
- Diagnose and resolve reliability, performance, and scaling bottlenecks in production pipelines supporting partnership deliverables.
What You’ll Need to Succeed
- Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience).
- 8+ years of professional software engineering experience in Python (or comparable systems languages).
- Proven experience designing, building, and operating scalable production systems — APIs, data pipelines, orchestration, and cloud infrastructure.
- Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices.
- Experience building or operating scientific or ML-adjacent infrastructure — workflow orchestration, experiment tracking, and reproducible pipelines.
- Hands-on experience with containerization, orchestration platforms, and infrastructure-as-code on a major cloud provider.
- Track record of leading technical direction across multiple systems and partnering deeply with research scientists or ML engineers to translate scientific needs into production engineering.
Bonus Points For
- Experience building infrastructure for protein design and engineering, antibody engineering, or other molecular ML applications.
- Familiarity with biological data formats and bioinformatics tooling.
- Experience integrating ML training/inference systems with broader product or scientific platforms.
- Open-source contributions to scientific computing or data infrastructure projects.
Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$162,800 - $200,200 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We’re All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Your Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), software engineers build the systems that connect generative models, scientific data, and experimental workflows into reliable, production-grade pipelines powering Lila's protein design and engineering campaigns.
We're hiring a Staff ML Engineer, Life Sciences AI to lead software infrastructure development for our protein design and engineering pipelines. This is a senior IC role focused on the engineering systems that surround and support our ML stack — pipeline orchestration, data flow between computational and experimental systems, integration of new tools and methods, and the developer experience that lets LSAI move fast on commercial partnership deliverables.
What You'll Be Building
- Architect and build software infrastructure powering Lila's protein design and engineering pipelines: orchestration, data flow, APIs, and integration with experimental systems.
- Own the engineering side of LSAI's "Lab-in-the-Loop" lifecycle — connecting computational outputs to experimental inputs and feeding results back into design workflows.
- Onboard new tools and methods developed by AI scientists and ML engineers into production-ready systems used in commercial partnership campaigns.
- Partner cross-functionally with ML researchers, scientists, and platform engineers to translate research code into reliable, scalable systems.
- Set engineering standards for LSAI software — design reviews, CI/CD, testing, observability, reproducibility — and mentor senior engineers as the team grows.
- Diagnose and resolve reliability, performance, and scaling bottlenecks in production pipelines supporting partnership deliverables.
What You’ll Need to Succeed
- Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience).
- 8+ years of professional software engineering experience in Python (or comparable systems languages).
- Proven experience designing, building, and operating scalable production systems — APIs, data pipelines, orchestration, and cloud infrastructure.
- Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices.
- Experience building or operating scientific or ML-adjacent infrastructure — workflow orchestration, experiment tracking, and reproducible pipelines.
- Hands-on experience with containerization, orchestration platforms, and infrastructure-as-code on a major cloud provider.
- Track record of leading technical direction across multiple systems and partnering deeply with research scientists or ML engineers to translate scientific needs into production engineering.
Bonus Points For
- Experience building infrastructure for protein design and engineering, antibody engineering, or other molecular ML applications.
- Familiarity with biological data formats and bioinformatics tooling.
- Experience integrating ML training/inference systems with broader product or scientific platforms.
- Open-source contributions to scientific computing or data infrastructure projects.
Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$162,800 - $200,200 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We’re All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
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Get Access To All JobsTips for Finding Visa Sponsorship as a Machine Learning Engineer
Map your degree to SOC codes
H-1B1 visa eligibility hinges on your degree matching the specific role. Pull the O*NET profile for Machine Learning Engineers and confirm your transcript reflects the field listed. Mismatches on the LCA are a common rejection trigger.
Target employers with active LCA filings
Use Migrate Mate to filter for companies that have filed Labor Condition Applications for machine learning or data science roles. Employers already familiar with H-1B1 Singapore filings move faster and make fewer documentation errors.
Verify your offer letter specifies specialty occupation
Your offer letter must describe duties that require a directly related bachelor's degree or higher. A vague title like 'engineer' without role-specific language can stall DOL certification. Ask your employer to mirror the O*NET task descriptions.
Check prevailing wage before negotiating salary
The LCA requires your employer to pay at least the prevailing wage for your location and role. Look up the current rate using OFLC Wage Search before your offer stage so you're not renegotiating after the employer submits the LCA to DOL.
Prepare NUS or NTU transcripts for equivalency review
Singaporean three-year degree programs sometimes prompt consular officers to request an equivalency evaluation. Have your academic records ready and, if needed, a credential evaluation letter confirming your qualification meets U.S. bachelor's-level standards.
Confirm your employer is E-Verify enrolled before your start date
Unlike H-1B, the H-1B1 doesn't require USCIS petition approval, but your employer still needs to complete E-Verify verification once you start. Confirm enrollment before you resign your current position to avoid onboarding delays.
Frequently Asked Questions
Does a Machine Learning Engineer role qualify as a specialty occupation for H-1B1 Singapore?
Yes. Machine Learning Engineer is classified under computer and information research science occupations, which consistently meet the specialty occupation standard requiring a directly related bachelor's degree. The role's core duties, model development, algorithm design, and statistical analysis, map clearly to degrees in computer science, mathematics, or statistics. Bring documentation connecting your specific job duties to your academic field when you attend your consulate interview.
How does the H-1B1 Singapore differ from the H-1B for machine learning roles?
The H-1B1 Singapore skips the annual lottery entirely and has a dedicated cap of 5,400 visas per year that has never been exhausted. You apply directly at the U.S. consulate in Singapore rather than through a USCIS petition, which cuts months off the timeline. The trade-off is that H-1B1 doesn't support dual intent, so demonstrated immigrant intent can complicate renewals.
How do I find employers actively willing to sponsor H-1B1 Singapore for ML roles?
Search Migrate Mate for companies with Labor Condition Application filing history in machine learning and data science categories. Employers who have filed LCAs before understand the H-1B1 process and are less likely to withdraw offers due to unfamiliarity with Singapore-specific visa procedures. Prioritize companies in tech, finance, and AI research where H-1B1 Singapore sponsorship is more established.
Can I switch employers on an H-1B1 Singapore while staying in the U.S.?
Yes, but the process restarts with your new employer. Your new employer must file a fresh LCA with DOL for your new role and location, and you'll typically need to return to Singapore for a new consulate appointment rather than changing status from within the U.S. Plan for a gap between roles and confirm your new employer's timeline before resigning.
What happens if my H-1B1 Singapore approval is delayed and my start date passes?
The H-1B1 doesn't have a formal cap-gap provision like the H-1B, so consulate processing delays directly affect your start date. Most consulates in Singapore process H-1B1 applications within a few weeks, but administrative processing can extend this. Communicate delays to your employer immediately and confirm whether they can adjust your start date, since the LCA has a specific period of authorized employment.
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