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
This Machine Learning Engineer for the Physical Sciences team focuses on building and operating end-to-end, scalable machine learning workflows that solve a diversity of scientific use cases in materials, chemistry, and physical sciences. Your work will advance research efforts on state-of-the-art algorithms to build towards scientific superintelligence across today’s greatest challenges in physical sciences.
What You'll Be Building
- Design, implement, and maintain end‑to‑end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring).
- Productionize models and services with robust testing, observability, and documentation in collaboration with cross-functional software teams and build CI/CD workflows and automated evaluations to ensure safe, frequent releases.
- Collaborate with domain scientists and platform engineers to translate research insights into performant, scalable systems.
- Contribute to technical design reviews, coding standards, and mentoring of best practices.
What You’ll Need To Succeed
- BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience.
- Strong Python software engineering fundamentals (testing, packaging, typing); experience with machine learning frameworks (e.g., PyTorch, Huggingface, etc.).
- Experience deploying ML services to production in cloud-based infrastructure (FastAPI/GRPC, containers, orchestration, cloud infra).
- Hands-on experience with model deployment in production systems (LLMs, multimodal models, databases, RAG) with strong debugging and profiling skills.
- Clear communication and collaboration in cross-functional settings.
Bonus Points For
- Exposure to scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks.
- GPU optimization experience (CUDA, Triton, compilation, distributed training).
- Prior contributions to open-source ML or scientific software.
- Experience with workflow orchestration, data provenance, or large-scale compute environments.
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:
$128,000 USD - $198,000 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
This Machine Learning Engineer for the Physical Sciences team focuses on building and operating end-to-end, scalable machine learning workflows that solve a diversity of scientific use cases in materials, chemistry, and physical sciences. Your work will advance research efforts on state-of-the-art algorithms to build towards scientific superintelligence across today’s greatest challenges in physical sciences.
What You'll Be Building
- Design, implement, and maintain end‑to‑end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring).
- Productionize models and services with robust testing, observability, and documentation in collaboration with cross-functional software teams and build CI/CD workflows and automated evaluations to ensure safe, frequent releases.
- Collaborate with domain scientists and platform engineers to translate research insights into performant, scalable systems.
- Contribute to technical design reviews, coding standards, and mentoring of best practices.
What You’ll Need To Succeed
- BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience.
- Strong Python software engineering fundamentals (testing, packaging, typing); experience with machine learning frameworks (e.g., PyTorch, Huggingface, etc.).
- Experience deploying ML services to production in cloud-based infrastructure (FastAPI/GRPC, containers, orchestration, cloud infra).
- Hands-on experience with model deployment in production systems (LLMs, multimodal models, databases, RAG) with strong debugging and profiling skills.
- Clear communication and collaboration in cross-functional settings.
Bonus Points For
- Exposure to scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks.
- GPU optimization experience (CUDA, Triton, compilation, distributed training).
- Prior contributions to open-source ML or scientific software.
- Experience with workflow orchestration, data provenance, or large-scale compute environments.
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:
$128,000 USD - $198,000 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.
See all 646+ H-1B1 Singapore Visa Machine Learning Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B1 Singapore Visa Machine Learning Engineer Jobs.
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 visa 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 visa 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.