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Machine Learning Engineer II / Senior Machine Learning Engineer I, Physical Sciences

Lila Sciences - Cambridge, MA USA

Posted Feb 3, 2026

Benefits

Parental leave
Not verified
Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
Offered From the posting source checked Jun 20, 2026
Verification
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Salary
$128K-$198K From the posting source checked Jun 20, 2026
401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$116,543 U.S. median for this role
Projected growth (BLS Employment Projections)
+9.8% - Much faster than average

40% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Engineering From the posting source checked Jun 20, 2026
Seniority
Senior From the posting source checked Jun 20, 2026

Schedule

Shift type
Not verified
Weekend work
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Company

Equity
Offered From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
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Deadline
Not stated

Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Engineer II / Senior Machine Learning Engineer I, Physical Sciences Cambridge, MA USA 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 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

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