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Biomolecular Science Jobs (NOW HIRING)

Umd

Open Rank Faculty

College Park, MD

They will join efforts to build up signature research themes including multidimensional biomolecular science, emergent materials &; sustainability, and multi-scale molecular science. The Department ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

Debswapna Bhattacharya's research group in the Department of Computer Science at Virginia Tech ( seeks to recruit multiple postdoctoral associates in Artificial Intelligence (AI) in Biomolecular ...

Pace

Principal Scientist- NMR Spectroscopy

Morrisville, NC · On-site

$110K - $120K/yr

Join our mission to continuously move science forward; to innovate and advance all aspects of our ... Leads biomolecular NMR characterization of protein and oligonucleotide samples, including sample ...

IQVIA

Senior Scientist

Durham, NC

$80K - $167K/yr

Supervise, train, and mentor team members in the design and execution of biomolecular interaction ... science field, preferably within biotech or pharmaceutical environments. * Extensive handson ...

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How much do biomolecular science jobs pay per year?

As of Jul 2, 2026, the average yearly pay for biomolecular science in the United States is $48,391.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,500.00 and $52,000.00 per year, depending on experience, location, and employer.

What is a Biomolecular Science job?

A Biomolecular Science job involves studying the structure, function, and interactions of biomolecules such as proteins, DNA, and enzymes. Professionals in this field work in biotechnology, pharmaceuticals, healthcare, and academic research to develop new treatments, diagnostics, and biotechnological applications. Roles may include laboratory research, drug development, genetic engineering, or bioinformatics. These jobs require expertise in molecular biology, chemistry, and data analysis to understand and manipulate biological systems for scientific and medical advancements.

What can you do with a biomolecular science degree?

A biomolecular science degree prepares individuals for careers in research, healthcare, pharmaceuticals, biotechnology, and diagnostics. Graduates can work as laboratory technicians, research scientists, quality control analysts, or in regulatory roles, often utilizing skills in laboratory techniques, data analysis, and scientific communication.

What are the key skills and qualifications needed to thrive in the Biomolecular Science position, and why are they important?

To excel in Biomolecular Science, individuals typically need a strong background in biology, chemistry, and molecular techniques, usually supported by a relevant bachelor's or advanced degree. Familiarity with laboratory instruments such as PCR machines, spectrophotometers, and experience using data analysis software like MATLAB or Python is often required, along with certifications in laboratory safety. Strong analytical thinking, attention to detail, and effective communication skills help differentiate outstanding professionals in this field. These competencies enable accurate experimentation, reliable data interpretation, and successful collaboration on research projects.

What does a typical day look like for someone working in Biomolecular Science?

A typical day in Biomolecular Science involves designing and conducting laboratory experiments, analyzing biological samples, and interpreting complex data related to molecular processes. You’ll often collaborate with other scientists, participate in research meetings, and document your findings in detailed lab notebooks or reports. The work environment is usually fast-paced and research-oriented, requiring adaptability as projects evolve. On many teams, you may also have opportunities to present results, contribute to scientific papers, and brainstorm innovative solutions to scientific challenges.

Is biomolecular science a good degree?

Biomolecular science is a valuable degree for careers in research, healthcare, and biotechnology, often leading to roles such as laboratory technician, research scientist, or biotechnologist. It provides a strong foundation in biology, chemistry, and laboratory skills, which are essential for working in scientific environments and pursuing advanced studies or certifications.

What biology jobs pay over $100k?

Biomolecular scientists working in roles such as research directors, senior biochemists, or biotech executives often earn over $100,000 annually. These positions typically require advanced degrees, specialized skills, and experience in laboratory management, drug development, or biotechnology industries.

What does a biomolecular scientist do?

A biomolecular scientist studies the structure, function, and interactions of biological molecules such as proteins, nucleic acids, and lipids. They often work in laboratories using techniques like spectroscopy, chromatography, and molecular biology methods to understand biological processes and develop medical or biotechnological applications.
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What cities are hiring for Biomolecular Science jobs? Cities with the most Biomolecular Science job openings:
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Biomolecular Science jobs near you
Infographic showing various Biomolecular Science job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 96% Full Time, and 3% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $48,391 per year, or $23.3 per hour.

ML Research Scientist - Atomistic Simulation Models

Achira

New York, NY • On-site

$164K - $259K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Invent and exploit probabilistic generative models that exploit to Achira's foundation simulation models for drug discovery to accelerate generative molecular design and biomolecular conformational sampling.
Why Achira
  • Join a world-class team of researchers, scientists, and engineers unifying probabilistic AI/ML and molecular simulation to reimagine small molecule drug discovery.
  • Advance new architectures for conditional 3D generation and learned proposal mechanisms informed by physical priors.
  • Operate at the frontier scale of large models, large datasets, and high-throughput evaluation on an ML-framework-native biomolecular simulation stack.
  • Own impact end-to-end from model conception to sampler design to prospective design tools.
  • Work in a culture that rewards rigor, speed, and scientific depth with an ownership mindset.

About the Role
Achira is building foundation simulation models and conditional generators for molecular systems. You will design probabilistic generative models (utilizing strategies such as diffusion models, normalizing flows, and flow matching) that that exploit Achira's next-generation biomolecular simulation potentials. Your work will enable target- and property-conditioned small-molecule generation and efficient exploration of biomolecular conformational landscapes, driving measurable gains in efficiency for small molecule design.
Familiarity with statistical mechanics-particularly nonequilibrium statistical mechanics based on Crooks/Jarzynski viewpoints-is desirable, but the center of gravity is probabilistic AI/ML.
What You'll Do
  • Develop conditional molecular generators: Build conditional small-molecule generators (e.g., pocket/scaffold/pharmacophore- and property-conditioned) using generative modeling strategies such as diffusion models, normalizing flows, and flow matching with 3D- and symmetry-aware representations.
  • Develop efficient samplers: Develop sequential sampling pipelines (e.g. SMC/AIS/tempering/Boltzmann generators) that anneal from learned priors into probabilities induced by Achira's ML potentials, maximizing ESS and reducing bias/variance.
  • Couple learning and sampling: Design learned proposal mechanisms (transport maps, score-guided moves) that adapt to stiff, multimodal landscapes and improve mixing and wall-clock efficiency.
  • Leverage nonequilibrium statistical mechanics: Where beneficial, use nonequilibrium switching protocols and work-based estimators to accelerate exploration and estimate partition-function ratios/affinity proxies.
  • Measure what matters: Define and track relevant metrics (ESS/compute, acceptance probabilities) and build reliable evaluation harnesses for fast, physics-informed feedback.
  • Experiment and engineer for reproducibility: Collaborate with our engineering team to implement robust research software in Python (PyTorch and/or JAX), with tests, CI, experiment tracking, and clear documentation.
  • Collaborate closely: Partner with computational chemistry, AI/ML, and platform teams to shape objectives (potency, selectivity, developability) and run prospective design studies.
  • Automate workflows: Use generative coding and experiment-management tools to accelerate iteration and close active-learning loops with synthetic data generation in the loop.

About You
  • Probabilistic ML background: Deep grasp of probabilistic machine learning, Markov chain Monte Carlo, variational inference, diffusion models, normalizing flows, flow matching, and uncertainty quantification.
  • Sequential methods expert: Experience with sequential Monte Carlo methods, proposal design, and diagnostics for high-dimensional, multimodal targets.
  • Geometric intuition: Comfort with graph/point-cloud/SE(3)-aware models and constraints relevant to protein-ligand systems and conformer generation.
  • Systems thinker: You integrate models into end-to-end pipelines (data → model → sampler → physics-aware evaluation → candidate triage) and care about measurable impact.
  • Familiarity with statistical mechanics(nice to have): Working knowledge of statistical mechanics, sampling, estimators, and the Crooks/Jarzynski perspective of nonequilibrium statistical mechanics will be a superpower.
  • Engineering discipline: Strong Python skills with PyTorch and/or JAX, Git/CI/testing, and reproducible experiment management.
  • Mindset: You value rigor, move with urgency, collaborate well, and enjoy turning ideas into reliable, high-impact tools.

• Minimum Qualifications
  • PhD (or equivalent research experience) in computer science, statistics, applied math, computational chemistry/biology, or related field.
  • Demonstrated track record in probabilistic ML and generative modeling (publications, impactful open-source, or deployed systems).
  • Industry experience tackling practical machine learning problems.
  • Hands-on experience with diffusion/flows/flow matching on structured or geometric data.
  • Practical experience with sequential Monte Carlo/AIS/tempering and/or advanced MCMC.
  • Proficiency in Python with PyTorch and/or JAX; strong software engineering hygiene.
  • Familiarity with biomolecular structure and data representations (graphs/3D/SMILES).

☆ Preferred Qualifications
  • Experience with ML interatomic/energy potentials is a bonus
  • Background in SE(3)-equivariant architectures, geometric deep learning, or score matching on manifolds.
  • Experience with active learning / Bayesian optimization or RL-style acquisition for proposal selection.
  • Experience with implementing MCMC sampling approaches grounded in statistical mechanics-especially nonequilibrium approaches that utilize Crooks/Jarzynski-a plus.
  • Contributions to open-source scientific software; experience mentoring or leading small research efforts.
Eligibility
In compliance with United States federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to provide required employment eligibility verification documentation upon hire.

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