STEM OPT Senior ML Engineer Jobs
Senior ML Engineer roles qualify for the 24-month STEM OPT extension when your degree falls under an eligible CIP code and your employer is enrolled in E-Verify. With 36 months of total OPT work authorization, you have a realistic runway to build production ML experience and secure H-1B visa sponsorship.
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Get Access To All JobsYour Impact at LILA
As a member of our team in the Physical Sciences division, you will shape the ML- and agent-driven infrastructure that enables Lila's scientific superintelligence to autonomously construct, execute, and interpret complex physics simulations. Your work will focus on building the core systems that allow AI agents to reason over and control computational experiments, spanning from electronic structure calculations to surrogate-driven atomistic modeling, and beyond. Your work will directly influence how autonomous computational processes will explore chemical and materials landscapes with unprecedented autonomy and robustness.
What You'll Be Building
- Architect and implement agentic frameworks that support dynamic, multi-stage simulation workflows for scientific tasks
- Develop pipelines enabling agents to autonomously plan, schedule, execute, and interpret computational tasks at scale
- Build integration layers and APIs that connect ML models, large-scale simulation engines, databases, and heterogeneous compute platforms
- Work with AI researchers to productionize agent behaviors, including tool-use strategies, simulation-aware decision loops, and adaptive task planning
- Improve the robustness, modularity, performance, and reproducibility of agent-driven computational workflows; build internal tooling for debugging, observability, and validation
What You'll Need to Succeed
- MS/PhD or equivalent experience in Computer Science, ML/AI, Scientific Computing, or a related technical field
- Strong experience building ML-driven pipelines, workflow systems, or tool-use frameworks, ideally for complex or scientific applications
- Proficiency in Python and ML ecosystems; experience with one or more compiled languages (e.g., C++, Rust, Julia) is beneficial
- Familiarity with large-scale scientific or engineering software, including integrating external tools into automated computational workflows
- Experience with distributed systems, HPC environments, cloud platforms, or accelerator-based computing
- Deep understanding of modern ML architectures and their deployment in production systems (e.g., GNNs, transformers, diffusion models, multimodal or tool-using models)
- Strong engineering fundamentals: reproducibility, testing, modular design, CI/CD, and scalable ML operations
Bonus Points For
- Experience developing or integrating agentic frameworks, autonomous ML pipelines, or multi-step tool-using agents, particularly for scientific applications
- Background in large-scale simulation frameworks, scientific workflow orchestration, or automated computational experiment platforms
- Contributions to open-source ML infrastructure, workflow engines, agent frameworks, or scientific software ecosystems
- Familiarity with data-centric engineering practices: streaming systems, provenance tracking, distributed metadata services, or large-scale data orchestration
- Experience with workflow orchestration systems (e.g., Flyte, Prefect, Airflow) and container orchestration (Kubernetes)
- Familiarity with materials science simulation codes (VASP, LAMMPS) or workflow frameworks (atomate, aiida) is a plus but not required
- Expertise in advanced ML techniques relevant to agentic reasoning (planning models, self-improving systems, multi-tool agents, RLHF/RLAIF workflows)
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
As a member of our team in the Physical Sciences division, you will shape the ML- and agent-driven infrastructure that enables Lila's scientific superintelligence to autonomously construct, execute, and interpret complex physics simulations. Your work will focus on building the core systems that allow AI agents to reason over and control computational experiments, spanning from electronic structure calculations to surrogate-driven atomistic modeling, and beyond. Your work will directly influence how autonomous computational processes will explore chemical and materials landscapes with unprecedented autonomy and robustness.
What You'll Be Building
- Architect and implement agentic frameworks that support dynamic, multi-stage simulation workflows for scientific tasks
- Develop pipelines enabling agents to autonomously plan, schedule, execute, and interpret computational tasks at scale
- Build integration layers and APIs that connect ML models, large-scale simulation engines, databases, and heterogeneous compute platforms
- Work with AI researchers to productionize agent behaviors, including tool-use strategies, simulation-aware decision loops, and adaptive task planning
- Improve the robustness, modularity, performance, and reproducibility of agent-driven computational workflows; build internal tooling for debugging, observability, and validation
What You'll Need to Succeed
- MS/PhD or equivalent experience in Computer Science, ML/AI, Scientific Computing, or a related technical field
- Strong experience building ML-driven pipelines, workflow systems, or tool-use frameworks, ideally for complex or scientific applications
- Proficiency in Python and ML ecosystems; experience with one or more compiled languages (e.g., C++, Rust, Julia) is beneficial
- Familiarity with large-scale scientific or engineering software, including integrating external tools into automated computational workflows
- Experience with distributed systems, HPC environments, cloud platforms, or accelerator-based computing
- Deep understanding of modern ML architectures and their deployment in production systems (e.g., GNNs, transformers, diffusion models, multimodal or tool-using models)
- Strong engineering fundamentals: reproducibility, testing, modular design, CI/CD, and scalable ML operations
Bonus Points For
- Experience developing or integrating agentic frameworks, autonomous ML pipelines, or multi-step tool-using agents, particularly for scientific applications
- Background in large-scale simulation frameworks, scientific workflow orchestration, or automated computational experiment platforms
- Contributions to open-source ML infrastructure, workflow engines, agent frameworks, or scientific software ecosystems
- Familiarity with data-centric engineering practices: streaming systems, provenance tracking, distributed metadata services, or large-scale data orchestration
- Experience with workflow orchestration systems (e.g., Flyte, Prefect, Airflow) and container orchestration (Kubernetes)
- Familiarity with materials science simulation codes (VASP, LAMMPS) or workflow frameworks (atomate, aiida) is a plus but not required
- Expertise in advanced ML techniques relevant to agentic reasoning (planning models, self-improving systems, multi-tool agents, RLHF/RLAIF workflows)
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 198+ STEM OPT Senior ML Engineer Jobs
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Get Access To All JobsTips for Finding STEM OPT Authorization as a Senior ML Engineer
Verify your CIP code before applying
Check your degree's CIP code against the DHS STEM OPT designated degree list before targeting Senior ML Engineer roles. Computer science, electrical engineering, and statistics degrees typically qualify, but applied mathematics subfields vary by program classification.
Filter jobs by E-Verify enrollment status
Senior ML Engineer postings don't always disclose E-Verify enrollment. Run the employer name through the E-Verify employer search before accepting a screening call so you don't spend three rounds of interviews on an ineligible company.
Frame your ML specialization in LCA job duty language
When reviewing offer letters, confirm the job duties reference model development, training pipelines, or deployment infrastructure. DOL LCA filings for Senior ML Engineers that use vague duty language face higher OFLC scrutiny, which can delay your start date.
Draft your I-983 training plan before the offer stage
Prepare a draft I-983 that maps your STEM OPT training objectives to specific ML engineering deliverables like model optimization or MLOps workflows. Employers unfamiliar with STEM OPT move faster when you arrive with a near-complete plan rather than explaining the form from scratch.
Use Migrate Mate to target employers with STEM OPT hiring history
Search Senior ML Engineer roles on Migrate Mate, which surfaces employers filtered for E-Verify enrollment and prior STEM OPT and H-1B filing activity. This narrows your list to companies already set up for the compliance steps your authorization requires.
Time your STEM OPT application around H-1B cap registration
If your initial OPT expires before October 1, file your STEM OPT extension with your DSO at least 90 days early so cap-gap coverage keeps you authorized through the H-1B start date. USCIS requires the extension application to be timely filed for cap-gap to apply.
Frequently Asked Questions
Does a Senior ML Engineer role qualify for the STEM OPT extension?
Yes, if your degree is in a DHS-designated STEM field such as computer science, statistics, or electrical engineering, and the Senior ML Engineer role involves work that directly relates to that field. The employer must also be enrolled in E-Verify. Your DSO confirms eligibility by matching your degree's CIP code to the official STEM designated degree list before recommending the extension on your I-20.
How do I confirm my employer is enrolled in E-Verify?
Use the E-Verify employer search tool to look up your prospective employer by company name or federal contractor status before signing an offer. E-Verify enrollment is a hard requirement for STEM OPT, not a preference. If the company isn't enrolled, they must complete enrollment before your STEM OPT start date, which adds processing time you'll want to account for before your initial OPT expires.
What goes in the I-983 training plan for a Senior ML Engineer?
Your I-983 must describe how the Senior ML Engineer role provides practical training in a STEM field directly related to your degree. Specific deliverables work better than job descriptions: include model architecture projects, data pipeline ownership, or production deployment responsibilities. Both you and a company supervisor must sign it, and your employer must report your training progress to your DSO every six months throughout the STEM OPT period.
What happens to my STEM OPT authorization if my employer loses E-Verify enrollment?
If your employer's E-Verify participation is terminated after your STEM OPT begins, your work authorization is no longer valid for that employer and you must stop working. USCIS expects continuous E-Verify enrollment throughout the STEM OPT period. Your DSO should be notified immediately, and you'll need to find a new E-Verify-enrolled employer or pursue a change of status if you have another option available.
How do I find Senior ML Engineer jobs that support STEM OPT?
Migrate Mate lists Senior ML Engineer roles filtered for employers enrolled in E-Verify and with documented sponsorship history, which removes the guesswork of identifying compliant companies. Because STEM OPT requires both E-Verify enrollment and a qualifying STEM role tied to your degree, starting with employers already familiar with the process shortens the time between offer and your training plan being signed.